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• Small Business

Best Small Business Loans

Find the right small business loan for you, with low rates and flexible terms

Small business loans are an integral component of the business world. Business owners rely on them to invest in new equipment or supplies, cover payroll, or manage cash flow. With a small business loan, business owners and entrepreneurs can access a lump sum of cash or a line of credit, which they can use to remain operational and continue growing their business for years to come.

The best small business loans have convenient application and funding processes, with competitive fees and flexible terms. Investopedia compared more than 20 of the top small business loan lenders based on their loan products, rates, fees, eligibility requirements, and overall transparency, among other factors. See our top-rated lenders below.

Best Small Business Loans of 2024

• Best Lender Comparison Site: Lendio
• Best for Business Checking: Kabbage
• Best Revolving Line of Credit: Fundbox
• Best for Microloans: Kiva
• Best for SBA Loans: Fundera
• Best for Same-Day Funding: OnDeck

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Methodology

Best lender comparison site : lendio.

• Mobile app: Yes
• Best perk: 24-hour funding
• Loan/LOC amount: $25,000-$500,000

We chose Lendio as one of the best loan comparison sites based on the fact that they let you compare loan options from more than 75 lenders in one place, more than other marketplace sites. You can also have your loan funded in as little as 24 hours, which can be a major boon for business owners who need cash fast.

Compare loan offers from more than 75 lenders

Funding in as little as 24 hours

Small business loans, SBA loans, and business lines of credit available

Lendio does not lend money directly.

Eligibility requirements vary, depending on the lender.

Lendio is a loan marketplace , so it won't be lending you funds directly, and we believe they shine in this category since they let you compare loan offers with more than 75 lenders in the small business space. Having the chance to have lenders compete for your business is the best way to make sure you get a small business loan with the best rate and terms you can possibly qualify for.

Lendio was founded in 2011, yet it has grown dramatically since then. So far, they claim to have funded over 300,000 small business loans worth more than $12 billion, and they don't plan to stop anytime soon.

In terms of their small business loan offering, Lendio lets you borrow between $25,000 and $500,000, depending on your needs. You can repay your loan over one to five years, and your interest rate could be as low as 4.5%. Lendio has just a 15-minute application process, and you can get a decision on the same business day, making it an efficient place to shop for a loan.

Best for Business Checking : Kabbage

• Best perk: Business checking
• Loan/LOC amount: Up to $150,000

We chose Kabbage as best for business checking thanks to its comprehensive package of tools for your small business, including a checking account and mobile app.

Transparent qualification requirements

No application fees

Multiple small business loan options available

Access to a mobile app

Business checking available

You need to be in business for one year.

You must have at least $4,200 in revenue each month (or $50,000 in annual revenue).

Founded in 2008, Kabbage is a technology company that aims to provide cash flow to businesses of all sizes. While many online companies focus on small business lending, we believe Kabbage stands out due to its plethora of small business loan options and its added features and benefits. 

Kabbage offers a variety of small business loans and options for a line of credit, including both unsecured and secured options. You can also apply for industry-specific loans for trucking, pawnshops, retail, and more. Kabbage prides itself on its easy online application process and fast funding for those who are approved.

Loan amounts from Kabbage vary depending on the type of loan you apply for. Kabbage also offers business lines of credit up to $150,000 and online loans in amounts from $500 to $150,000. Other loan types might offer larger limits.

Kabbage Funding also offers repayment terms of six and 12 months, depending on your needs. With Kabbage, there are no hidden loan fees or prepayment penalties, either. You will pay a monthly fee, but they are upfront about it.

Best Revolving Line of Credit : Fundbox

• Best perk: Decision in minutes

Fundbox focuses on offering lines of credit for business owners, and they make it possible to get prequalified online without a hard inquiry on your credit report. Their focus on business lines of credit makes them an expert on this particular type of small business funding. They offer decisions in minutes as well as funding as soon as the next business day.

Get prequalified online without a hard inquiry on your credit report

Get a decision in minutes

Only borrow what you need, unlike a small business loan that offers a lump sum

Lines of credit are only available for up to $150,000.

We chose Fundbox as the lender offering the best revolving line of credit, which allows you to qualify for a specific amount and borrow only what you need. Fundbox was founded in 2013, and the company uses technology to facilitate its B2B lines of credit with the goal of helping small businesses achieve significant success.

Lines of credit from Fundbox are only available in amounts up to $150,000, but you can get prequalified online without completing a full loan application. Once you apply, you can get a credit decision within minutes, and you’re under no obligation to accept the loan funds. 

Because Fundbox focuses on lines of credit, you get the chance to borrow only as much money as you need. Funds can transfer from your line of credit to your business checking account as soon as the next business day. This provider gives you the option to save money by paying off your balances early and ahead of schedule without any prepayment fees. 

Fundbox lets you see the fees for your line of credit upfront, and they’ll automatically debit your bank account to pay your amount due, so you won’t have to think about it. You get the option to repay your line of credit over 12 or 24 weeks.

Best for Microloans : Kiva

• Best perk: Zero percent interest
• Loan/LOC amount: Up to $15,000

Kiva is the best option for small business owners who only need to borrow a small amount of money. Their microloans currently come with a 0% interest rate, and you can borrow up to $15,000.

Borrow money with no interest

Get the chance to market your product to a growing community of Kiva lenders, currently 1.6 million strong

Repay your loan for up to 36 months

You can only borrow up to $15,000 with Kiva.

It can take 30 days or longer for your loan to be funded.

We chose this lender as the best option for microloans based on the fact that you can borrow money at 0% APR and repay it over three full years. Founded in 2005, Kiva aims to help underserved communities and their members qualify for the small business funding they need to get their dreams off the ground. So far, 2.1 million Kiva lenders have funded more than $1.74 billion in loans to more than 4.3 million borrowers in 77 countries around the globe. 

Interestingly, Kiva is not a bank but is instead a peer-to-peer lending platform. Kiva loans are geared to disadvantaged entrepreneurs, so investors who lend money through the platform get the chance to help people around the world.

In terms of their loan product, Kiva only lets you borrow up to $15,000. You can apply online in 20 to 30 minutes and earn the ability to advertise your funding project on their marketplace within 30 days; you get the chance to repay your loan over 36 months.

Kiva also lists easy requirements to qualify, including living in the U.S. and being at least 18 years old. To qualify for a Kiva loan, you also have to agree to use your loan for business purposes only.

Best for SBA Loans : Fundera

• Mobile app: No
• Best perk: Many lenders
• Loan/LOC amount: Up to $5.5 million for an SBA loan

We chose Fundera as the best option for Small Business Administration (SBA) loans due to the fact that it offers the most SBA options, including loans through the popular SBA 7(a) loan program. You can apply for an SBA loan directly on Fundera with your loan funded through a variety of top SBA lenders.

Fundera offers an array of business loans and lines of credit, including loan offers through the SBA.

Compare loan options through multiple lenders in one place

Gauge your ability to qualify without a hard inquiry on your credit report

Fundera is a small business marketplace and not a direct lender, meaning the company won’t actually fund your loan itself.

Credit score of at least 550 required

Collateral required for some SBA loans and loan amounts

Fundera is an excellent option for consumers hoping to qualify for an SBA loan, mostly because it lets you fill out a single application and compare multiple SBA loan options in one place. Founded in 2013, Fundera is a loan marketplace instead of a direct lender. This means the company connects small business owners with the best small business loans and lines of credit on the market today, but it does not lend money itself.

Because Fundera is a marketplace, it can offer nearly any type of business loan or line of credit available today. This includes loans through the Small Business Administration, which tend to come with flexible repayment terms and affordable interest rates.

Loan amounts and repayment terms vary, but it’s possible to qualify for an SBA loan in amounts up to $5.5 million. You may also be able to repay your loan over a period of up to 25 years, although it can take two weeks to get your loan funded. Note that SBA loans may require collateral, and that’s especially true for larger loan amounts.

Best for Same-Day Funding : OnDeck

• Best perk: Same-day funding
• Loan/LOC amount: Up to $250,000 for a loan; up to $100,000 for a line of credit

We chose OnDeck due to the fact they offer small business loans with funding as soon as the same business day. This can be crucial for business owners who need fast access to cash to keep up with business expenses or pay for an important piece of equipment right away.

Funding available as soon as the same business day

Borrow up to $250,000 with a small business loan or up to $100,000 with a line of credit

OnDeck is transparent about loan details and eligibility requirements.

Minimum personal credit score of 600 required

Applicants need at least one year in business with a minimum of $100,000 in annual business revenue.

Since its founding in 2006, OnDeck has grown to become one of the most prominent small business lenders offering capital on the market today. We chose OnDeck as best for same-day funding due to its easy online application process and rapid approval and fulfillment of your small business loan.

OnDeck makes it easy to qualify for a line of credit in amounts from $6,000 to $100,000 or a small business loan in amounts from $5,000 to $250,000. Repayment terms on small business loans are available for up to 24 months, and you will benefit from transparent pricing and no prepayment penalties.

Meanwhile, you can repay one of OnDeck's lines of credit over 12 months, plus you get the benefit of borrowing only what you need. Either option can work for small business owners who need access to capital, and both types of funding let you apply and receive a decision within the day.

OnDeck lists some basic requirements to qualify for a business loan , which include a minimum personal credit score of at least 600 for a long-term loan. You also need to be in business for at least two years and you need to have a least $250,000 in annual business revenue to qualify.

Compare Small Business Loans

Frequently asked questions, what is a business loan.

A business loan is a type of loan business owners take out in order to access funds for inventory, payroll, and other business expenses. Business loans come in many different forms, although traditional small business loans tend to be offered in one lump sum and with a fixed monthly payment and interest rate.

However, there are other types of funding available for small businesses . Lines of credit are a popular option since they let business owners borrow only what they need. Further, invoice factoring can be helpful for some business owners who are short on cash but have plenty of unpaid invoices to use as collateral.

Small business owners can also apply for an SBA loan , which is a type of loan that is backed by the Small Business Administration. While the SBA does not loan money themselves, they connect borrowers with SBA-approved lenders that offer SBA-backed loans with competitive rates and terms.

Are You Personally Liable for an SBA Loan?

When applying for an SBA loan, you must provide an unlimited personal guarantee if you own 20% or more of the business. This means that if you fail to repay the loan because the business fails, you are still on the hook for repaying the entire loan amount. Also note that, in the case of a borrower defaulting, the lender can recover 50% to 85% of the outstanding loan balance from the SBA.

What Can I Use a Small Business Loan For?

Approved applicants can use their small business loan funds to pay for any ongoing expenses as well as payroll or investments in equipment or inventory. Small business loans are also frequently used as a means to manage or smooth out cash flow in times when expenses exceed income. If you’re a small business owner who needs to buy some equipment but you’re waiting to be paid by a few major clients, for example, it could help you keep up with your expenses and buy equipment while you wait for your invoices to be paid.

Other popular uses of small business loans include raising capital to hire new staff or to launch a new marketing or ad campaign that could help boost revenue.

We looked at more than 20 lenders who offer small business loans, small business lines of credit, and unique funding options like invoice factoring. We looked for lenders who offer transparency when it comes to their loan products and eligibility requirements, and we also looked for lenders who post fees and ongoing costs prominently on their websites. 

To find the best small business loans, we compared loan options and lenders to find companies with excellent ratings and user reviews on websites like Trustpilot. We also looked for lenders who offer high loan amounts and flexible repayment options, as well as the ability to apply and set up loan funding online.

PixelsEffect / Getty Images

Lendio. " Business Loan Types | Lendio ."

Kabbage. " Kabbage Funding Loans ."

Kiva. " How Kiva US works ."

Small Business Administration. " 504 Loan Program - Small Business Administration ."

Small Business Administration. " Loan Fact Sheet - Small Business Administration ."

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25+ Essential Small Business Lending Statistics [2023]: What Percentage Of SBA Loans Get Approved

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Research Summary. Whether you need property, renovations, or are simply looking for some investment money, having money loaned out can be a regular part of owning a small business. These statistics can help you understand the trends behind small business lending in the US:

Large, nonlocal banks are responsible for 89.5% of smaller loans (less than $100,000) given to small businesses.

The average SBA loan is $417,316 , while the maximum loan amount is $5 million.

SBA loans have a 49% approval rate at small banks.

SBA loans only have a 25% approval rate at large banks.

33% of small business owners struggle or fail due to a lack of capital.

In 2020 the SBA distributed over 14 million loans worth $764 billion to small businesses.

Small Business Financial Statistics

Despite the rewards, it can also be expensive to run a small business in the U.S. Additionally, many who have great ideas can only afford to jumpstart them with small business loans.

Overall, when it comes to the financial status of American small businesses, here are the facts:

As a whole, only 48% of small businesses in the U.S. have their financing needs met.

That number includes the 20% of small businesses that achieved financing through loans and 28% that didn’t have sufficient capital without loans. On the other hand, a considerable 52% of small businesses receive no financing, only receive a portion of the financing they need or have too much debt to apply for loans.

At least 70% of small businesses in the U.S. have outstanding debt.

While that means nearly 3/4 of small businesses are in debt in the U.S., it’s also important to note the varying amounts of debt. 38% of small businesses with debt owe less than $100,000, with that number being divided into 17% owing $1 to $25,000 and 21% owing between $25,000 to $100,000.

The average Small Business Administration (SBA) loan is $417,316.

However, the maximum loan amount for a standard loan is up to $5 million, with other smaller SBA loan types capping out between $350,000 and $500,000. Additionally, most loans over $25,000 require the lender to take some form of collateral, including trading assets.

Only 38% of small businesses took out a loan to expand in 2020.

The COVID-19 pandemic hit small businesses particularly hard, with the percentage taking out loans to expand reducing from 58% in 2019 to 38% in 2020. Instead, many small businesses have had to take out loans just to stay afloat.

The average interest rate for a small business loan is between 2.54% – 7.01%.

A majority of this disparity is connected to which bank, program, or administration the loan comes from. For instance, SBA loans have average interest rates between 5.50% to 8%.

Only 33% of small businesses will survive ten years in business.

While the majority, 79.8%, can survive their first year. Unfortunately, survival rates continue to decrease over time until a small business reaches the ten-year mark, with 69.2% surviving two years and 50.2% making it five years.

Small Business Loan Statistics

Many businesses needed loans to expand, and during the height of the COVID-19 pandemic, many also needed loans to survive. Here are the facts about small business loans:

Overall, the average small business loan is $633,000.

This includes smaller regional and large banks , but it’s important to note the massive disparity between possible loan amounts. A small business can generally receive anything from $13,000 to $1.2 million from banks.

In 2020 the SBA distributed over fourteen million loans worth $764 billion to small businesses.

And of that $764 billion, $736 billion fell under the category of COVID-19 relief loans. That means that 96% of the loans distributed by the SBA in 2020 were for COVID-19 relief.

The average small business loan from alternative lenders is between $50,000-$80,000.

These lenders are typically private companies that operate online. Some of the most well-known alternative lenders include Fundbox, BlueVine, OnDeck, Credibly, Balboa Capital, QuarterSpot, and Funding Circle.

The average loan amount from large national banks is $593,000.

Unlike small regional banks, which only loan out an average of $146,000 to small businesses. At the other extreme, foreign banks give an average of $8.5 million to small businesses.

Small Business Loan Application Statistics

No one enjoys applying for loans, even if it’s necessary. However, it’s a common process, with just under half of all small businesses needing to apply for one. Here are the facts about small business loan applications, according to our research:

43% of small businesses applied for a loan in 2020.

Most of these businesses were those who needed COVID-19 relief, meaning that nearly half of American small businesses deeply struggled through the height of the pandemic.

Loan approval rates for small businesses applying to large banks are only 13.8%.

Whereas the approval rates from small banks are up to 19%, and non-bank loans have an approval rating of 24.7%. Overall, all of these approval rates are much lower than the rates for SBA loans.

32% of small businesses applying for loans now apply to non-bank lenders.

More and more small businesses are applying for loans from non-bank lenders, as this percentage is up from 24% in 2017 and 19% in 2016.

20% of small business loans are denied due to credit issues.

If a small business has weak or non-existing credit, this can have a huge impact on loan approval odds. It’s important to note that your business’s credit isn’t necessarily your personal credit, so you should watch both.

Small Business Loan Demographics

Given that there are huge disparities between the rural areas and cities in the U.S., it’s unsurprising that small businesses in different areas can have different outcomes. Additionally, the disparities between race, gender, and class can also play a role. Some common small business loan demographics include:

Rural small businesses are more likely to receive loans, with 51% acquiring all of the financing requested.

On the other hand, only 38% of urban small businesses receive the full financing amount they requested. This is especially interesting, given that only 17% of small businesses operate in rural areas.

Rural small businesses rely more heavily on small banks, with 62% of loan applications going to them.

Comparatively, 53% of urban small businesses apply for loans from larger banks , with only 43% reaching out to smaller banks. Of course, this isn’t surprising when you consider that 55% of the banks in rural areas are small banks, compared to only 25% in cities.

Black-owned businesses receive less than 2% of small business loans.

This is despite Black Americans making up 13% of the total population. In fact, studies have shown that Black-owned firms are twice as likely to see loan rejections, with less than 47% of financing applications being approved.

Women-owned small businesses receive only 16% of small business loans.

And this is even though women own roughly 30% of small companies. Overall, research has shown that women are more likely to be rejected or face more stringent terms than their male counterparts.

Small Business Loan Statistics by Industry

The type of industry a small business is in can also impact whether or not that business will be approved for loans. After all, the U.S. is home to small businesses that deal in anything from retail , fitness, car repair, and more. Overall, when it comes to small business loans by industry, here are the facts:

The construction and renovation industry receives the highest proportion of small business loans, roughly 15%.

Similarly, the second highest is Transportation and Trucking small businesses, receiving around 15% of small business loans. These industries tend to need loans to cover equipment, repairs, maintenance, and even turn a profit.

Full-service restaurants receive the highest volume of SBA small businesses loans, reaching 28,680 in 2019 alone.

Additionally, the limited-service restaurant businesses hold the #2 spot, with 19,141 loans distributed. This massive number of loans totaled over $17.1 billion combined. For context, the closest runner-up to the restaurant industries was the dental industry, which only amounted to 10,699 loans totaling $6 billion.

At least 11 potentially legal industries are not eligible for SBA small business loans.

These include Gambling, Government-Owned, Lending Loan Packaging Firms, Multi-Sales Distribution, Nonprofit, MLM, Real Estate Investment firms, Religious, and Speculation-Based industries.

Small Business Lending FAQ

What percent of small business loans are approved?

General small business loans have a 57% approval rate, while SBA loans, in particular, have a higher or lower approval rate based on the size of the bank. Overall though, approval odds can be affected by the applicant’s location and the type of bank used, as well as race or gender.

For example, rural small businesses have 51% of applications approved, compared to only 38% of urban businesses.

This can be explained by the fact that urban businesses favor large banks, with approval ratings as low as 13.8%. With the approval rating for small banks being nearly 1.5x higher than that.

Additionally, Black Americans and other minorities also struggle with approval rates, as only 47% of loans requested by Black individuals are approved.

How are most small businesses financed?

Most small businesses are financed through the owner’s personal investments. This can include anything from their savings to personal assets. Even in 2020, when loan applications peaked, only 43% of small businesses applied for a small business loan.

However, when small businesses do require loans, the four most common ways of being financed are through: the SBA, large national banks, small regional banks, and alternative lenders. Financing through these lenders can be anything from a few thousand dollars to over a million.

Is small business lending profitable?

Small business lending is sometimes profitable, but also risky. Due to increasing interest rates , small business loans can indeed be very profitable for lenders. Plus, a $500,000 loan might take as long as a mortgage to pay off, creating a large amount of time for that interest to add up.

On the other hand, small loans (less than $25,000) can be incredibly unprofitable for lenders, and if a lender sees a business venture as too risky, they probably won’t finance it. That’s because if the business collapses, the bank will lose its entire investment.

How much debt does the average small business have?

In the United States, the average small business owner is roughly $195,000 in debt. However, it’s important to note that a small business debt shouldn’t exceed more than 30% of your business capital .

How do small businesses qualify for loans?

There are four basic steps small businesses should take to qualify for loans. These include:

Building Credit. The first step to qualifying for any loan is building an attractive credit score, and small businesses are no different. You can either choose to improve your own personal credit or create solid business credit.

Research Qualifications & Requirements. You won’t know if you qualify for a loan if you don’t research it. Research any loan you’re interested in and understand the key differences between federal and bank loans.

Gather Documents. You’ll need to have the required financial and legal documents on hand if you want to qualify for a small business loan. This can include anything from personal, and business income tax returns to commercial licenses.

Show Lenders a Business Plan. Showing potential investors your business plan is a great way to build confidence. These plans include things like: company description, product/service description, industry analysis, facilities and operations plans, Current and projected financials, marketing strategies, and more.

Can you get a business loan with no revenue?

Yes, you can get a small business loan with no revenue. Though it may be more difficult, it is possible to achieve a small business loan with no revenue. After all, many businesses need loans to start operating.

With that in mind, here are some steps you can take to get a small business loan when you have no revenue:

Credit. Investors love high credit scores. If you have one, they’ll be far more likely to trust you with a loan.

Financial Projections. If you can at least somewhat accurately project how much money your business will make, lenders will feel more comfortable lending you money.

Business Plan. Like financial projections, a business plan will tell investors about your product/service, so they can decide whether or not they believe it’s worth investing in.

What credit score do I need for an SBA loan?

You should try to have a credit score of at least 690 or higher if you plan to apply for an SBA loan. Having a score between 690-720 will give you good odds of landing a loan, while scores of 720+ will give you great odds.

Due to the impact of the COVID-19 pandemic on small businesses, the process of acquiring loans is more important than ever before. After all, 33% of small business owners struggle or fail due to a lack of capital, and only 48% have their financing needs met. In 2020 alone, 43% of small businesses applied for small business loans.

Of course, there is a disparity between loans and lenders. While the average small business loan is between $400,000 and $650,000, depending on the lender, loans can theoretically be as low as a few thousand dollars or as high as a million. And, with approval ratings no higher than 57%, it’s no surprise that a portion of applications is denied due to credit and capital issues.

Luckily, there are several avenues small businesses can take to acquire financing, such as reaching out to the SBA, large national banks, small regional banks, and alternative lenders.

Federal Reserve Banks. “Small Business Credit Survey.” Accessed on November 22nd, 2021.

SBA. “Types of 7(a) loans.” Accessed on November 22nd, 2021.

Nerdwallet. “Average Business Loan Rate: What to Know About Interest Costs.” Accessed on November 22nd, 2021.

Entrepreneur . “The True Failure Rate of Small Businesses.” Accessed on November 22nd, 2021.

ValuePenguin. “Average Small Business Loan Amount: Across Banks and Alternative Lenders.” Accessed on November 23rd, 2021.

SBA. “SBA Achieves Historic Small Business Lending for Fiscal Year 2020 with More Than $17 Billion in SBA Seattle District.” Accessed on November 23rd, 2021.

Small Business Trends. “Small Business Loan Approval Rates Up at Big Banks.” Accessed on November 23rd, 2021.

FRB. “Access to Financial Services Matters to Small Businesses.” Accessed on November 23rd, 2021.

FORA Financial. “4 Steps to Take If You Aren’t Approved for an SBA Loan.” Accessed on November 23rd, 2021.

Fundera. “Rural Small Businesses Earn Better Profits and More Financing vs. City Ones.” Accessed on November 23rd, 2021.

The Guardian. “Black-owned firms are twice as likely to be rejected for loans. Is this discrimination?” Accessed on November 23rd, 2021.

Ondeck. “How the Gender Gap Affects Small Business Loan Approvals.” Accessed on November 23rd, 2021.

FORA Financial. “The Industries Most Likely to Ask for a Business Loan and Why.” Accessed on November 23rd, 2021.

WestTown Bank Trust. “The Top 40 Industries for SBA Financing in 2020.” Accessed on November 23rd, 2021.

SBA. “Does Your Industry Qualify for an SBA 7(a) Loan? Will You Benefit?” Accessed on November 23rd, 2021.

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Jack Flynn is a writer for Zippia. In his professional career he’s written over 100 research papers, articles and blog posts. Some of his most popular published works include his writing about economic terms and research into job classifications. Jack received his BS from Hampshire College.

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Filing instructions guide for small business lending data collected in 2024

As a result of ongoing litigation, all deadlines for compliance with the small business lending rule currently are stayed for all covered financial institutions.

1. What is the filing instructions guide?

The 2024 filing instructions guide is a set of resources to help you file small business lending data with the Consumer Financial Protection Bureau (CFPB) in 2025 covering the period from October 1, 2024 to December 31, 2024. These resources are briefly described in this section and are further detailed throughout this web page in individual sections.

These resources may be useful for employees in a variety of roles, for example:

• Staff who collect, prepare, and submit data
• Technology support staff
• Compliance officers

The guide includes the following sections:

Filing process overview.

Section 2 provides an overview of the process to file small business lending data with the CFPB. It describes the data submission platform (the platform), which is the system that filers will use to submit their data. It also describes the file format that will be required for submitting the data.

Data points

Section 3 provides instructions for what to enter into each data field in the small business lending application register (register). A machine-readable version of the data specification is provided.

Data validation

Section 4 lists the validation requirements that a register must meet before it can be filed with the CFPB. A machine-readable version of the validation specification is provided.

Where to get help

Section 5 provides a summary of resources available from the CFPB to assist with small business lending rule-related inquiries.

2. Filing process overview

This section provides instructions on filing small business lending data with the CFPB. This document is not a substitute for the small business lending rule, found in Regulation B (12 CFR part 1002), Subpart B. Refer to the rule for guidance and clarification regarding the reporting requirements for each data field.

2.1. About the small business lending platform

Filers will submit their data to the platform via a web interface. There will be a process for individuals representing a financial institution to register for an account to access the online submission platform.

Using the platform, each filer will provide financial institution identifying information per 12 CFR § 1002.109(b). The platform will walk filers through the small business lending application register filing process, including uploading data, performing validation checks on the data, and certifying the data. An authorized representative of the filer with knowledge of the data submitted will certify to the accuracy and completeness of the data submitted.

2.2. File format

Your register must be submitted in a comma-separated values (CSV) file format.

Your CSV file should adhere to the following standards:

• The register must be a comma-delimited text file.
• The first line of the file is a header row. The contents of the header row must be the column names specified in the Data points section of this guide, in the order of the field numbering used in the guide, separated by commas.
• Each following line of the file represents a covered application record. Each record in the file must contain the data fields described in the Data points section of this guide, in order, corresponding to the order of the column names in the header row.
• Each data field within each row must be separated with a comma (","). That means that if you leave a field blank, the field should still be denoted by commas (example: three fields containing 1, [blank], 3 would be formatted as 1,,3 ).
• If any field contains space(s) (" ") before and/or after the comma delimiter, the space(s) will be ignored.
• This is not a fixed-width formatted file. Do not include leading zeros, tabs, or spaces for the purpose of making a data field a specific number of characters.
• If a field contains a comma character, the field must be enclosed in double quotes (e.g., "Confederated Tribes of the Coos, Lower Umpqua and Siuslaw Indians of Oregon"). Fields not containing a comma can also be enclosed in double quotes, but this is not required.
• No field in a row may contain a line break, newline, or carriage-return. Line breaks should only appear at the end of a row. Each row of the file should represent a whole application record.
• Files must use UTF-8 encoding (note that all-ASCII files are always valid UTF-8).

Any file not conforming to these specifications cannot be submitted as a register.

3. Data points

This section provides instructions on entering data in the small business lending application register for small business lending data collected in 2024. This document is not a substitute for the rule, found in Regulation B (12 CFR part 1002), Subpart B. Refer to the rule for guidance and clarification regarding the reporting requirements for each data field.

Data fields are presented below in the order they are recorded in the register. For a machine-readable view of the data specification, see the following link:

Data spec (CSV)

3.1 Unique identifier

Rule section: 12 CFR 1002.107(a)(1)

Field 1: Unique identifier

Column name, instructions.

• Field type: Text (width 21 to 45 characters)
• Required for all application records
• Begins with the financial institution's Legal Entity Identifier as defined in comment 1002.109(b)(6)-1
• May be uppercase letters, numerals, or a combination of uppercase letters and numerals (cannot contain dashes, other special characters, or characters with diacritics)
• Must be unique within the financial institution
• Must not include any information that could be used to directly identify the applicant or borrower
• 10BX939C5543TQA1144M999143938

Validations

• Must be at least 21 characters in length and at most 45 characters in length
• May contain any combination of numbers and/or uppercase letters (i.e., 0-9 and A-Z), and must not contain any other characters
• May not be used in more than one record within a small business lending application register
• The first 20 characters should match the Legal Entity Identifier (LEI) for the financial institution

3.2 Application date

Rule section: 12 CFR 1002.107(a)(2)

Field 2: Application date

• Field type: Date
• For October 1, 2024, enter 20241001
• Must be a real calendar date using YYYYMMDD format

3.3 Application method

Rule section: 12 CFR 1002.107(a)(3)

Field 3: Application method

• Field type: Single response
• Must equal 1, 2, 3 or 4

3.4 Application recipient

Rule section: 12 CFR 1002.107(a)(4)

Field 4: Application recipient

• Must equal 1 or 2

3.5 Credit type

Rule section: 12 CFR 1002.107(a)(5)

Field 5: Credit product

Field 6: free-form text field for other credit products, field 7: type of guarantee, field 8: free-form text field for other guarantee, field 9: loan term: na/np flag, field 10: loan term.

Rule section: 12 CFR 1002.107(a)(5)(i)

• Must equal 1, 2, 3, 4, 5, 6, 7, 8, 977, or 988
• Field type: Text (width up to 300 characters)
• Conditionally required if 'credit product' is code 977. Leave blank if code 977 is not entered.
• Must not exceed 300 characters in length

Rule section: 12 CFR 1002.107(a)(5)(ii)

• Field type: Multiple response
• Each value (separated by semicolons) must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 977, or 999
• Must contain at least one and at most five values, separated by semicolons
• When code 999 is reported, should not contain any other values
• Should not contain duplicated values
• Conditionally required if 'type of guarantee' contains code 977. Leave blank if code 977 is not entered.

Rule section: 12 CFR 1002.107(a)(5)(iii)

• Must equal 900, 988, or 999
• Field type: Numeric
• Conditionally required if 'credit type: loan term: NA/NP flag' is code 900. Leave blank if code 900 is not entered.
• For a loan term of 36 months, enter 36
• For a loan term of less than 1 month, enter 1
• When present, must be a whole number
• When present, must be greater than or equal to 1
• When present, should be less than 1200 (100 years)

3.6 Credit purpose

Rule section: 12 CFR 1002.107(a)(6)

Field 11: Credit purpose

• Each value (separated by semicolons) must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 977, 988, or 999
• Must contain at least one and at most three values, separated by semicolons
• When code 988 or 999 is reported, should not contain any other values

Field 12: Free-form text field for other credit purpose

• Conditionally required if 'credit purpose' contains code 977. Leave blank if code 977 is not entered.

3.7 Amount applied for

Rule section: 12 CFR 1002.107(a)(7)

Field 13: Amount applied for: NA/NP flag

• Must equal 900, 988 or 999

Field 14: Amount applied for

• Conditionally required if 'amount applied for: NA/NP flag' is code 900. Leave blank if code 900 is not entered.
• The dollar amount for initial amount of credit/credit limit requested by applicant at the application stage
• If application is in response to a firm offer that specifies an amount or limit, the dollar amount of the firm offer, unless the applicant requested a different amount
• If application is in response to a firm offer that does not specify an amount or limit and the applicant did not request a specific amount, the dollar amount underwritten
• If application is in response to a firm offer that specifies an amount or limit as a range and the applicant did not request a specific amount, the dollar amount underwritten
• If applicant did not request a particular amount but the financial institution underwrites for a specific amount, the dollar amount underwritten
• If applicant requested a range of dollar amounts, the midpoint of that range
• If application is a request for additional amounts on an existing account, the dollar amount of additional credit requested
• For $12,345, enter 12345
• When present, must be a numeric value
• When present, must be greater than 0

3.8 Amount approved or originated

Rule section: 12 CFR 1002.107(a)(8)

Field 15: Amount approved or originated

• Conditionally required if 'action taken' is code 1 or 2. Report not applicable by leaving blank if codes 1 or 2 are not entered.
• For a closed-end origination, the amount of the originated loan
• For a closed-end application, the highest amount approved if the application was approved but not accepted
• For an open-end origination, the amount of the credit limit established
• For an open-end application, the highest amount approved if the application was approved but not accepted
• For additional credit amounts that were approved for or originated on an existing account, report the additional credit amount approved or originated, and not any previous amount extended
• Leave blank if amount approved or originated is not applicable because an application is denied, withdrawn, or incomplete
• For $101.23, enter 101.23

3.9 Action taken

Rule section: 12 CFR 1002.107(a)(9)

Field 16: Action taken

• Must equal 1, 2, 3, 4, or 5

3.10 Action taken date

Rule section: 12 CFR 1002.107(a)(10)

Field 17: Action taken date

• For October 25, 2024, enter 20241025
• The date indicated must occur within the current reporting period: October 1, 2024 to December 31, 2024

3.11 Denial reasons

Rule section: 12 CFR 1002.107(a)(11)

Field 18: Denial reason(s)

• If Cashflow, enter 4
• If Cashflow, Collateral, Time in business, and Government loan program criteria, enter 4;5;6;7 or 7;5;6;4 etc.
• Each value (separated by semicolons) must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 977, or 999
• Must contain at least one and at most four values, separated by semicolons

Field 19: Free-form text field for other denial reason(s)

• Conditionally required if 'denial reason(s)' contains code 977. Leave blank if code 977 is not entered.

3.12 Pricing information

Rule section: 12 CFR 1002.107(a)(12)

Field 20: Interest rate type

Field 21: initial rate period, field 22: fixed rate: interest rate, field 23: adjustable rate transaction: margin, field 24: adjujstable rate transaction: index name, field 25: adjustable rate transaction: index name: other, field 26: adjustable rate transaction: index value, field 27: total origination charges, field 28: amount of total broker fees, field 29: initial annual charges, field 30: mca/sales-based: additional cost for merchant cash advances or other sales-based financing: na flag, field 31: mca/sales-based: additional cost for merchant cash advances or other sales-based financing, field 32: prepayment penalty could be imposed, field 33: prepayment penalty exists.

Rule section: 12 CFR 1002.107(a)(12)(i)

• Must equal 1, 2, 3, 4, 5, 6, or 999

Rule section: 12 CFR 1002.107(a)(12)(i)(B)

• Conditionally required if 'interest rate type' is code 3, 4, 5, or 6. Leave blank if codes 3, 4, 5, or 6 are not entered.

Rule section: 12 CFR 1002.107(a)(12)(i)(A)

• Conditionally required if 'interest rate type' is code 2, 4, or 6. Leave blank if codes 2, 4, or 6 are not entered.
• If 4.125%, enter 4.125
• If 4.500%, enter 4.5, 4.50, or 4.500
• When present, should generally be greater than 0.1
• Conditionally required if 'interest rate type' is code 1, 3, or 5. Leave blank if codes 1, 3, or 5 are not entered.
• If 2.525%, enter 2.525
• If 2.500%, enter 2.5, 2.50, or 2.500
• Must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 977 or 999
• Conditionally required if 'adjustable rate transaction: index name' is code 977. Leave blank if code 977 is not entered.
• Conditionally required if 'interest rate type' is code 1 or 3. Leave blank if codes 1 or 3 are not entered.
• If 1.025%, enter 1.025
• If 3.100%, enter 3.1, 3.10, or 3.100

Rule section: 12 CFR 1002.107(a)(12)(ii)

• If $2,500, enter 2500 or 2500.00
• If $2,582.91, enter 2582.91
• If $0, enter 0
• If -$100, enter -100

Rule section: 12 CFR 1002.107(a)(12)(iii)

• If $1,125, enter 1125 or 1125.00
• If $1,125.76, enter 1125.76

Rule section: 12 CFR 1002.107(a)(12)(iv)

• If $1,034, enter 1034 or 1034.00
• If $1,034.97, enter 1034.97

Rule section: 12 CFR 1002.107(a)(12)(v)

• Must equal 900 or 999
• Conditionally required if 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing: NA flag' is code 900. Leave blank if code 900 is not entered.
• If $3,500, enter 3500 or 3500.00
• If $3,527.14, enter 3527.14

Rule section: 12 CFR 1002.107(a)(12)(vi)(A)

• Must equal 1, 2 or 999

Rule section: 12 CFR 1002.107(a)(12)(vi)(B)

3.13 Census tract

Rule section: 12 CFR 1002.107(a)(13)

Field 34: Type of address

• Must equal 1, 2, 3 or 988

Field 35: Tract number

• Field type: Special (width 11 characters)
• Conditionally required if 'type of address' is code 1, 2, or 3. Leave blank if code 988 is entered.
• Address where the loan proceeds will principally be applied, if known
• If the proceeds address is not known, location of borrower's main office or headquarters
• If neither of those addresses are known, another address or location associated with the applicant
• 06037264000 (a census tract within Los Angeles County, CA)
• When present, must be a GEOID with exactly 11 digits
• When present, should be a valid census tract GEOID as defined by the U.S. Census Bureau

3.14 Gross annual revenue

Rule section: 12 CFR 1002.107(a)(14)

Field 36: Gross annual revenue: NP flag

• Must equal 900 or 988

Field 37: Gross annual revenue

• Conditionally required if 'gross annual revenue: NP flag' is code 900. Leave blank if code 900 is not entered.
• If $855,430, enter 855430 or 855430.00
• If $855,430.17, enter 855430.17

3.15 North American Industry Classification System (NAICS) code

Rule section: 12 CFR 1002.107(a)(15)

Field 38: North American Industry Classification System (NAICS) code: NP flag

Field 39: north american industry classification system (naics) code.

• Field type: Special (width 3 characters)
• Conditionally required if 'North American Industry Classification System (NAICS) code: NP flag' is code 900. Leave blank if code 900 is not entered.
• 311 (a business engaged in the food processing sector)
• When present, must be exactly three numeric characters
• When present, should be a valid NAICS code

3.16 Number of workers

Rule section: 12 CFR 1002.107(a)(16)

Field 40: Number of workers

• Must equal 1, 2, 3, 4, 5, 6, 7, 8, 9 or 988

3.17 Time in business

Rule section: 12 CFR 1002.107(a)(17)

Field 41: Type of response

• Must equal 1, 2, 3, or 988

Field 42: Time in business

• Conditionally required if 'time in business: type of response' is code 1. Leave blank if code 1 is not entered.
• When present, must be greater than or equal to 0

3.18 Minority-owned, women-owned, and LGBTQI+-owned business statuses

Rule section: 12 CFR 1002.107(a)(18)

Field 43: Business ownership status

• If women-owned, enter 2
• If women-owned and LGBTQI+-owned, enter 2;3 or 3;2
• If LGBTQI+-owned and the applicant responded that they did not wish to provide this information, enter 3
• Each value (separated by semicolons) must equal 1, 2, 3, 955, 966, or 988
• Must contain at least one value
• When code 966 or 988 is reported, should not contain any other values

3.19 Number of principal owners

Rule section: 12 CFR 1002.107(a)(20)

Field 44: Number of principal owners: NP flag

Field 45: number of principal owners.

• Conditionally required if 'number of principal owners: NP flag' is code 900. Leave blank if code 900 is not entered.
• When present, must equal 0, 1, 2, 3, or 4

3.20 Demographic information of principal owner 1

Rule section: 12 CFR 1002.107(a)(19)

Field 46: Ethnicity of principal owner 1

Field 47: ethnicity of principal owner 1: free-form text field for other hispanic or latino ethnicity, field 48: race of principal owner 1, field 49: race of principal owner 1: free-form text field for american indian or alaska native enrolled or principal tribe, field 50: race of principal owner 1: free-form text field for other asian race, field 51: race of principal owner 1: free-form text field for other black or african american race, field 52: race of principal owner 1: free-form text field for other pacific islander race, field 53: sex/gender of principal owner 1: np flag, field 54: sex/gender of principal owner 1: free-form text field for self-identified sex/gender.

• Conditionally required if there is at least one principal owner. Report not applicable by leaving blank if there are no principal owners.
• If Mexican, enter 11
• If Mexican and Puerto Rican, enter 11;12 or 12;11
• If Mexican and the applicant responded that they did not wish to provide this information, enter 11
• If responded Argentinean in the free form-text field but the applicant did not select Other Hispanic or Latino ethnicity, enter 977. May also enter 14.
• When present, each value (separated by semicolons) must equal 1, 11, 12, 13, 14, 2, 966, 977, or 988
• Conditionally required if 'ethnicity of principal owner 1' contains code 977. Report not applicable by leaving blank if code 977 is not entered.
• If Haitian and White, enter 33;5 or 5;33
• If Asian and the applicant responded that they did not wish to provide this information, enter 2.
• If responded Thai in the free form-text field for other Asian race but the applicant did not select Other Asian race, enter 972. May also enter 27.
• When present, each value (separated by semicolons) must equal 1, 2, 21, 22, 23, 24, 25, 26, 27, 3, 31, 32, 33, 34, 35, 36, 37, 4, 41, 42, 43, 44, 5, 966, 971, 972, 973, 974, or 988
• Conditionally required if 'race of principal owner 1' contains code 971. Report not applicable by leaving blank if code 971 is not entered
• Conditionally required if 'race of principal owner 1' contains code 972. Report not applicable by leaving blank if code 972 is not entered
• Conditionally required if 'race of principal owner 1' contains code 973. Report not applicable by leaving blank if code 973 is not entered
• Conditionally required if 'race of principal owner 1' contains code 974. Report not applicable by leaving blank if code 974 is not entered
• Marshallese
• If the applicant responded in free-form text field and also responded that they did not wish to provide this information, enter 1.
• When present, must equal 1, 966, or 988
• Conditionally required if 'sex/gender of principal owner 1' is code 1. Report not applicable by leaving blank if code 1 is not entered.

3.21 Demographic information of principal owner 2

Field 55: ethnicity of principal owner 2, field 56: ethnicity of principal owner 2: free-form text field for other hispanic or latino ethnicity, field 57: race of principal owner 2, field 58: race of principal owner 2: free-form text field for american indian or alaska native enrolled or principal tribe, field 59: race of principal owner 2: free-form text field for other asian race, field 60: race of principal owner 2: free-form text field for other black or african american race, field 61: race of principal owner 2: free-form text field for other pacific islander race, field 62: sex/gender of principal owner 2: np flag, field 63: sex/gender of principal owner 2: free-form text field for self-identified sex/gender.

• Conditionally required if there are at least two principal owners. Report not applicable by leaving blank if there are fewer than two principal owners.
• Conditionally required if 'ethnicity of principal owner 2' contains code 977. Leave blank if code 977 is not entered.
• Conditionally required if 'race of principal owner 2' contains code 971. Leave blank if code 971 is not entered.
• Conditionally required if 'race of principal owner 2' contains code 972. Leave blank if code 972 is not entered.
• Conditionally required if 'race of principal owner 2' contains code 973. Leave blank if code 973 is not entered.
• Conditionally required if 'race of principal owner 2' contains code 974. Leave blank if code 974 is not entered.
• Conditionally required if 'sex/gender of principal owner 2' is code 1. Leave blank if code 1 is not entered.

3.22 Demographic information of principal owner 3

Field 64: ethnicity of principal owner 3, field 65: ethnicity of principal owner 3: free-form text field for other hispanic or latino ethnicity, field 66: race of principal owner 3, field 67: race of principal owner 3: free-form text field for american indian or alaska native enrolled or principal tribe, field 68: race of principal owner 3: free-form text field for other asian race, field 69: race of principal owner 3: free-form text field for other black or african american race, field 70: race of principal owner 3: free-form text field for other pacific islander race, field 71: sex/gender of principal owner 3: np flag, field 72: sex/gender of principal owner 3: free-form text field for self-identified sex/gender.

• Conditionally required if there are at least three principal owners. Report not applicable by leaving blank if there are fewer than three principal owners.
• Conditionally required if 'ethnicity of principal owner 3' contains code 977. Leave blank if code 977 is not entered.
• Conditionally required if 'race of principal owner 3' contains code 971. Leave blank if code 971 is not entered.
• Conditionally required if 'race of principal owner 3' contains code 972. Leave blank if code 972 is not entered.
• Conditionally required if 'race of principal owner 3' contains code 973. Leave blank if code 973 is not entered.
• Conditionally required if 'race of principal owner 3' contains code 974. Leave blank if code 974 is not entered.
• Conditionally required if 'sex/gender of principal owner 3' is code 1. Leave blank if code 1 is not entered.

3.23 Demographic information of principal owner 4

Field 73: ethnicity of principal owner 4, field 74: ethnicity of principal owner 4: free-form text field for other hispanic or latino ethnicity, field 75: race of principal owner 4, field 76: race of principal owner 4: free-form text field for american indian or alaska native enrolled or principal tribe, field 77: race of principal owner 4: free-form text field for other asian race, field 78: race of principal owner 4: free-form text field for other black or african american race, field 79: race of principal owner 4: free-form text field for other pacific islander race, field 80: sex/gender of principal owner 4: np flag, field 81: sex/gender of principal owner 4: free-form text field for self-identified sex/gender.

• Conditionally required if there are four principal owners. Report not applicable by leaving blank if there are fewer than four principal owners.
• Conditionally required if 'ethnicity of principal owner 4' contains code 977. Leave blank if code 977 is not entered.
• Conditionally required if 'race of principal owner 4' contains code 971. Leave blank if code 971 is not entered.
• Conditionally required if 'race of principal owner 4' contains code 972. Leave blank if code 972 is not entered.
• Conditionally required if 'race of principal owner 4' contains code 973. Leave blank if code 973 is not entered.
• Conditionally required if 'race of principal owner 4' contains code 974. Leave blank if code 974 is not entered.
• Conditionally required if 'sex/gender of principal owner 4' is code 1. Leave blank if code 1 is not entered.

4. Data validation

Data validations are a series of checks that run on a small business lending application register to ensure that the data entries are correct and ready to submit, meaning the data are both internally consistent and consistent with the syntax and logic specified by this guide. When data are uploaded to the small business lending data submission platform, before the register can be certified and submitted, the platform will review the submission to determine if the data pass the validations described in this section. What follows is a description of the types of validations that will be performed on a register prior to its certification and acceptance.

First, validations vary by type:

• An error validation checks that each data field contains valid data and that each value submitted matches the expected type. Each record must pass all of these validations in order for the register to be certified and submitted.
• A warning validation checks for values that could indicate a mistake in the register. These are quality checks to assist filers in checking that their register has been compiled correctly and alerting them to possible problems. The filer may confirm the accuracy of all values flagged by warning validations as part of the filing process in order to certify and submit their data.

Validations also vary by scope:

• Each single-field validation pertains to only one specific field in each record. These validations check that the data held in an individual field match the values that are expected. A single-field validation may be an error validation or a warning validation.
• Multi-field validations check that the values of certain fields make sense in combination with other values in the same record. These validations have a list of “affected data fields,” which are the individual fields within the record whose values will be compared to identify whether the record passes the validation checks. For example, many multi-field validations check for the presence of conditionally required data, meaning that such checks ensure that fields that should be blank are blank, and fields that should be populated are populated. A multi-field validation may be an error validation or a warning validation.
• There is also one register-level validation . This validation checks that the register does not contain duplicate IDs.

Below is a comprehensive list of validations that will be applied to each register before submission. For multi-field validations, we provide a pseudocode interpretation of each validation. This pseudocode is an illustration of the logic of the validation, exclusively for the purpose of ensuring that the validation logic is clear and unambiguous. It is not code which can be compiled and run. For a tabular view of the validation specification in CSV file format, see the following link:

Validation spec (CSV)

4.1. Single-field errors

Validation id: e0001, validation name.

• uid.invalid_text_length

Affected data field

Description.

• 'Unique identifier' must be at least 21 characters in length and at most 45 characters in length.

Validation ID: E0002

• uid.invalid_text_pattern
• 'Unique identifier' may contain any combination of numbers and/or uppercase letters (i.e., 0-9 and A-Z), and must not contain any other characters.

Validation ID: E0020

• app_date.invalid_date_format
• 'Application date' must be a real calendar date using YYYYMMDD format.

Validation ID: E0040

• app_method.invalid_enum_value
• 'Application method' must equal 1, 2, 3, or 4.

Validation ID: E0060

• app_recipient.invalid_enum_value
• app_recipient
• 'Application recipient' must equal 1 or 2.

Validation ID: E0080

• ct_credit_product.invalid_enum_value
• ct_credit_product
• 'Credit product' must equal 1, 2, 3, 4, 5, 6, 7, 8, 977, or 988.

Validation ID: E0100

• ct_credit_product_ff.invalid_text_length
• ct_credit_product_ff
• 'Free-form text field for other credit products' must not exceed 300 characters in length.

Validation ID: E0120

• ct_guarantee.invalid_enum_value
• ct_guarantee
• Each value in 'type of guarantee' (separated by semicolons) must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 977, or 999.

Validation ID: E0121

• ct_guarantee.invalid_number_of_values
• 'Type of guarantee' must contain at least one and at most five values, separated by semicolons.

Validation ID: E0140

• ct_guarantee_ff.invalid_text_length
• ct_guarantee_ff
• 'Free-form text field for other guarantee' must not exceed 300 characters in length.

Validation ID: E0160

• ct_loan_term_flag.invalid_enum_value
• ct_loan_term_flag
• 'Loan term: NA/NP flag' must equal 900, 988, or 999.

Validation ID: E0180

• ct_loan_term.invalid_numeric_format
• ct_loan_term
• When present, 'loan term' must be a whole number.

Validation ID: E0181

• ct_loan_term.invalid_numeric_value
• When present, 'loan term' must be greater than or equal to 1.

Validation ID: E0200

• credit_purpose.invalid_enum_value
• credit_purpose
• Each value in 'credit purpose' (separated by semicolons) must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 977, 988, or 999.

Validation ID: E0201

• credit_purpose.invalid_number_of_values
• 'Credit purpose' must contain at least one and at most three values, separated by semicolons.

Validation ID: E0220

• credit_purpose_ff.invalid_text_length
• credit_purpose_ff
• 'Free-form text field for other credit purpose' must not exceed 300 characters in length.

Validation ID: E0240

• amount_applied_for_flag.invalid_enum_value
• amount_applied_for_flag
• 'Amount applied For: NA/NP flag' must equal 900, 988, or 999.

Validation ID: E0260

• amount_applied_for.invalid_numeric_format
• amount_applied_for
• When present, 'amount applied for' must be a numeric value.

Validation ID: E0261

• amount_applied_for.invalid_numeric_value
• When present, 'amount applied for' must be greater than 0.

Validation ID: E0280

• amount_approved.invalid_numeric_format
• amount_approved
• When present, 'amount approved or originated' must be a numeric value.

Validation ID: E0281

• amount_approved.invalid_numeric_value
• When present, 'amount approved or originated' must be greater than 0.

Validation ID: E0300

• action_taken.invalid_enum_value
• action_taken
• 'Action taken' must equal 1, 2, 3, 4, or 5.

Validation ID: E0320

• action_taken_date.invalid_date_format
• action_taken_date
• 'Action taken date' must be a real calendar date using YYYYMMDD format.

Validation ID: E0321

• action_taken_date.invalid_date_value
• The date indicated by 'action taken date' must occur within the current reporting period: October 1, 2024 to December 31, 2024.

Validation ID: E0340

• denial_reasons.invalid_enum_value
• denial_reasons
• Each value in 'denial reason(s)' (separated by semicolons) must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 977, or 999.

Validation ID: E0341

• denial_reasons.invalid_number_of_values
• 'Denial reason(s)' must contain at least one and at most four values, separated by semicolons.

Validation ID: E0360

• denial_reasons_ff.invalid_text_length
• denial_reasons_ff
• 'Free-form text field for other denial reason(s)' must not exceed 300 characters in length.

Validation ID: E0380

• pricing_interest_rate_type.invalid_enum_value
• pricing_interest_rate_type
• 'Interest rate type' must equal 1, 2, 3, 4, 5, 6, or 999.

Validation ID: E0400

• pricing_init_rate_period.invalid_numeric_format
• pricing_init_rate_period
• When present, 'adjustable rate transaction: initial rate period' must be a whole number.

Validation ID: E0401

• pricing_init_rate_period.invalid_numeric_value
• When present, 'adjustable rate transaction: initial rate period' must be greater than 0.

Validation ID: E0420

• pricing_fixed_rate.invalid_numeric_format
• pricing_fixed_rate
• When present, 'fixed rate: interest rate' must be a numeric value.

Validation ID: E0440

• pricing_adj_margin.invalid_numeric_format
• pricing_adj_margin
• When present, 'adjustable rate transaction: margin' must be a numeric value.

Validation ID: E0460

• pricing_adj_index_name.invalid_enum_value
• pricing_adj_index_name
• 'Adjustable rate transaction: index name' must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 977, or 999.

Validation ID: E0480

• pricing_adj_index_name_ff.invalid_text_length
• pricing_adj_index_name_ff
• 'Adjustable rate transaction: index name: other' must not exceed 300 characters in length.

Validation ID: E0500

• pricing_adj_index_value.invalid_numeric_format
• pricing_adj_index_value
• When present, 'adjustable rate transaction: index value' must be a numeric value.

Validation ID: E0520

• pricing_origination_charges.invalid_numeric_format
• pricing_origination_charges
• When present, 'total origination charges' must be a numeric value.

Validation ID: E0540

• pricing_broker_fees.invalid_numeric_format
• pricing_broker_fees
• When present, 'amount of total broker fees' must be a numeric value.

Validation ID: E0560

• pricing_initial_charges.invalid_numeric_format
• pricing_initial_charges
• When present, 'initial annual charges' must be a numeric value.

Validation ID: E0580

• pricing_mca_addcost_flag.invalid_enum_value
• pricing_mca_addcost_flag
• 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing: NA flag' must equal 900 or 999.

Validation ID: E0600

• pricing_mca_addcost.invalid_numeric_format
• pricing_mca_addcost
• When present, 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing' must be a numeric value.

Validation ID: E0620

• pricing_prepenalty_allowed.invalid_enum_value
• pricing_prepenalty_allowed
• 'Prepayment penalty could be imposed' must equal 1, 2, or 999.

Validation ID: E0640

• pricing_prepenalty_exists.invalid_enum_value
• pricing_prepenalty_exists
• 'Prepayment penalty exists' must equal 1, 2, or 999.

Validation ID: E0660

• census_tract_adr_type.invalid_enum_value
• census_tract_adr_type
• 'Census tract: type of address' must equal 1, 2, 3, or 988.

Validation ID: E0680

• census_tract_number.invalid_text_length
• census_tract_number
• When present, 'census tract: tract number' must be a GEOID with exactly 11 digits.

Validation ID: E0700

• gross_annual_revenue_flag.invalid_enum_value
• gross_annual_revenue_flag
• 'Gross annual revenue: NP flag' must equal 900 or 988.

Validation ID: E0720

• gross_annual_revenue.invalid_numeric_format
• gross_annual_revenue
• When present, 'gross annual revenue' must be a numeric value.

Validation ID: E0740

• naics_code_flag.invalid_enum_value
• naics_code_flag
• 'North American Industry Classification System (NAICS) code: NP flag' must equal 900 or 988.

Validation ID: E0760

• naics_code.invalid_text_length
• When present, 'North American Industry Classification System (NAICS) code' must be three digits in length.

Validation ID: E0761

• naics_code.invalid_naics_format
• 'North American Industry Classification System (NAICS) code' may only contain numeric characters.

Validation ID: E0780

• number_of_workers.invalid_enum_value
• number_of_workers
• 'Number of workers' must equal 1, 2, 3, 4, 5, 6, 7, 8, 9, or 988.

Validation ID: E0800

• time_in_business_type.invalid_enum_value
• time_in_business_type
• 'Time in business: type of response' must equal 1, 2, 3, or 988.

Validation ID: E0820

• time_in_business.invalid_numeric_format
• time_in_business
• When present, 'time in business' must be a whole number.

Validation ID: E0821

• time_in_business.invalid_numeric_value
• When present, 'time in business' must be greater than or equal to 0.

Validation ID: E0840

• business_ownership_status.invalid_enum_value
• business_ownership_status
• Each value in 'business ownership status' (separated by semicolons) must equal 1, 2, 3, 955, 966, or 988.

Validation ID: E0841

• business_ownership_status.invalid_number_of_values
• 'Business ownership status' must contain at least one value.

Validation ID: E0860

• num_principal_owners_flag.invalid_enum_value
• num_principal_owners_flag
• 'Number of principal owners: NP flag' must equal 900 or 988.

Validation ID: E0880

• num_principal_owners.invalid_enum_value
• num_principal_owners
• When present, 'number of principal owners' must equal 0, 1, 2, 3, or 4.

Validation ID: E0900

• po_1_ethnicity.invalid_enum_value
• po_1_ethnicity
• When present, each value in 'ethnicity of principal owner 1' (separated by semicolons) must equal 1, 11, 12, 13, 14, 2, 966, 977, or 988.

Validation ID: E0920

• po_1_ethnicity_ff.invalid_text_length
• po_1_ethnicity_ff
• 'Ethnicity of principal owner 1: free-form text field for other Hispanic or Latino' must not exceed 300 characters in length.

Validation ID: E0940

• po_1_race.invalid_enum_value
• When present, each value in 'race of principal owner 1' (separated by semicolons) must equal 1, 2, 21, 22, 23, 24, 25, 26, 27, 3, 31, 32, 33, 34, 35, 36, 37, 4, 41, 42, 43, 44, 5, 966, 971, 972, 973, 974, or 988.

Validation ID: E0960

• po_1_race_anai_ff.invalid_text_length
• po_1_race_anai_ff
• 'Race of principal owner 1: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not exceed 300 characters in length.

Validation ID: E0980

• po_1_race_asian_ff.invalid_text_length
• po_1_race_asian_ff
• 'Race of principal owner 1: free-form text field for other Asian' must not exceed 300 characters in length.

Validation ID: E1000

• po_1_race_baa_ff.invalid_text_length
• po_1_race_baa_ff
• 'Race of principal owner 1: free-form text field for other Black or African American' must not exceed 300 characters in length.

Validation ID: E1020

• po_1_race_pi_ff.invalid_text_length
• po_1_race_pi_ff
• 'Race of principal owner 1: free-form text field for other Pacific Islander race' must not exceed 300 characters in length.

Validation ID: E1040

• po_1_gender_flag.invalid_enum_value
• po_1_gender_flag
• When present, 'sex/gender of principal owner 1: NP flag' must equal 1, 966, or 988.

Validation ID: E1060

• po_1_gender_ff.invalid_text_length
• po_1_gender_ff
• 'Sex/gender of principal owner 1: free-form text field for self-identified sex/gender' must not exceed 300 characters in length.

Validation ID: E1080

• po_2_ethnicity.invalid_enum_value
• po_2_ethnicity
• When present, each value in 'ethnicity of principal owner 2' (separated by semicolons) must equal 1, 11, 12, 13, 14, 2, 966, 977, or 988.

Validation ID: E1100

• po_2_ethnicity_ff.invalid_text_length
• po_2_ethnicity_ff
• 'Ethnicity of principal owner 2: free-form text field for other Hispanic or Latino' must not exceed 300 characters in length.

Validation ID: E1120

• po_2_race.invalid_enum_value
• When present, each value in 'race of principal owner 2' (separated by semicolons) must equal 1, 2, 21, 22, 23, 24, 25, 26, 27, 3, 31, 32, 33, 34, 35, 36, 37, 4, 41, 42, 43, 44, 5, 966, 971, 972, 973, 974, or 988.

Validation ID: E1140

• po_2_race_anai_ff.invalid_text_length
• po_2_race_anai_ff
• 'Race of principal owner 2: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not exceed 300 characters in length.

Validation ID: E1160

• po_2_race_asian_ff.invalid_text_length
• po_2_race_asian_ff
• 'Race of principal owner 2: free-form text field for other Asian' must not exceed 300 characters in length.

Validation ID: E1180

• po_2_race_baa_ff.invalid_text_length
• po_2_race_baa_ff
• 'Race of principal owner 2: free-form text field for other Black or African American' must not exceed 300 characters in length.

Validation ID: E1200

• po_2_race_pi_ff.invalid_text_length
• po_2_race_pi_ff
• 'Race of principal owner 2: free-form text field for other Pacific Islander race' must not exceed 300 characters in length.

Validation ID: E1220

• po_2_gender_flag.invalid_enum_value
• po_2_gender_flag
• When present, 'sex/gender of principal owner 2: NP flag' must equal 1, 966, or 988.

Validation ID: E1240

• po_2_gender_ff.invalid_text_length
• po_2_gender_ff
• 'Sex/gender of principal owner 2: free-form text field for self-identified sex/gender' must not exceed 300 characters in length.

Validation ID: E1260

• po_3_ethnicity.invalid_enum_value
• po_3_ethnicity
• When present, each value in 'ethnicity of principal owner 3' (separated by semicolons) must equal 1, 11, 12, 13, 14, 2, 966, 977, or 988.

Validation ID: E1280

• po_3_ethnicity_ff.invalid_text_length
• po_3_ethnicity_ff
• 'Ethnicity of principal owner 3: free-form text field for other Hispanic or Latino' must not exceed 300 characters in length.

Validation ID: E1300

• po_3_race.invalid_enum_value
• When present, each value in 'race of principal owner 3' (separated by semicolons) must equal 1, 2, 21, 22, 23, 24, 25, 26, 27, 3, 31, 32, 33, 34, 35, 36, 37, 4, 41, 42, 43, 44, 5, 966, 971, 972, 973, 974, or 988.

Validation ID: E1320

• po_3_race_anai_ff.invalid_text_length
• po_3_race_anai_ff
• 'Race of principal owner 3: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not exceed 300 characters in length.

Validation ID: E1340

• po_3_race_asian_ff.invalid_text_length
• po_3_race_asian_ff
• 'Race of principal owner 3: free-form text field for other Asian' must not exceed 300 characters in length.

Validation ID: E1360

• po_3_race_baa_ff.invalid_text_length
• po_3_race_baa_ff
• 'Race of principal owner 3: free-form text field for other Black or African American' must not exceed 300 characters in length.

Validation ID: E1380

• po_3_race_pi_ff.invalid_text_length
• po_3_race_pi_ff
• 'Race of principal owner 3: free-form text field for other Pacific Islander race' must not exceed 300 characters in length.

Validation ID: E1400

• po_3_gender_flag.invalid_enum_value
• po_3_gender_flag
• When present, 'sex/gender of principal owner 3: NP flag' must equal 1, 966, or 988.

Validation ID: E1420

• po_3_gender_ff.invalid_text_length
• po_3_gender_ff
• 'Sex/gender of principal owner 3: free-form text field for self-identified sex/gender' must not exceed 300 characters in length.

Validation ID: E1440

• po_4_ethnicity.invalid_enum_value
• po_4_ethnicity
• When present, each value in 'ethnicity of principal owner 4' (separated by semicolons) must equal 1, 11, 12, 13, 14, 2, 966, 977, or 988.

Validation ID: E1460

• po_4_ethnicity_ff.invalid_text_length
• po_4_ethnicity_ff
• 'Ethnicity of principal owner 4: free-form text field for other Hispanic or Latino' must not exceed 300 characters in length.

Validation ID: E1480

• po_4_race.invalid_enum_value
• When present, each value in 'race of principal owner 4' (separated by semicolons) must equal 1, 2, 21, 22, 23, 24, 25, 26, 27, 3, 31, 32, 33, 34, 35, 36, 37, 4, 41, 42, 43, 44, 5, 966, 971, 972, 973, 974, or 988.

Validation ID: E1500

• po_4_race_anai_ff.invalid_text_length
• po_4_race_anai_ff
• 'Race of principal owner 4: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not exceed 300 characters in length.

Validation ID: E1520

• po_4_race_asian_ff.invalid_text_length
• po_4_race_asian_ff
• 'Race of principal owner 4: free-form text field for other Asian' must not exceed 300 characters in length.

Validation ID: E1540

• po_4_race_baa_ff.invalid_text_length
• po_4_race_baa_ff
• 'Race of principal owner 4: free-form text field for other Black or African American' must not exceed 300 characters in length.

Validation ID: E1560

• po_4_race_pi_ff.invalid_text_length
• po_4_race_pi_ff
• 'Race of principal owner 4: free-form text field for other Pacific Islander race' must not exceed 300 characters in length.

Validation ID: E1580

• po_4_gender_flag.invalid_enum_value
• po_4_gender_flag
• When present, 'sex/gender of principal owner 4: NP flag' must equal 1, 966, or 988.

Validation ID: E1600

• po_4_gender_ff.invalid_text_length
• po_4_gender_ff
• 'Sex/gender of principal owner 4: free-form text field for self-identified sex/gender' must not exceed 300 characters in length.

4.2. Multi-field errors

Validation id: e2000.

• ct_credit_product_ff.conditional_field_conflict

Affected data fields

• When 'credit product' does not equal 977 (other), 'free-form text field for other credit products' must be blank.
• When 'credit product' equals 977, 'free-form text field for other credit products' must not be blank.

Validation ID: E2001

• ct_guarantee_ff.conditional_field_conflict
• When 'type of guarantee' does not contain 977 (other), 'free-form text field for other guarantee' must be blank.
• When 'type of guarantee' contains 977, 'free-form text field for other guarantee' must not be blank.

Validation ID: E2003

• ct_loan_term_flag.enum_value_conflict
• When 'credit product' equals 1 (term loan - unsecured) or 2 (term loan - secured), 'loan term: NA/NP flag' must not equal 999 (not applicable).
• When 'credit product' equals 988 (not provided by applicant and otherwise undetermined), 'loan term: NA/NP flag' must equal 999.

Validation ID: E2004

• ct_loan_term.conditional_field_conflict
• When 'loan term: NA/NP flag' does not equal 900 (applicable and reported), 'loan term' must be blank.
• When 'loan term: NA/NP flag' equals 900, 'loan term' must not be blank.

Validation ID: E2005

• credit_purpose_ff.conditional_field_conflict
• When 'credit purpose' does not contain 977 (other), 'free-form text field for other credit purpose' must be blank.
• When 'credit purpose' contains 977, 'free-form text field for other credit purpose' must not be blank.

Validation ID: E2007

• amount_applied_for.conditional_field_conflict
• When 'amount applied for: NA/NP flag' does not equal 900 (applicable and reported), 'amount applied for' must be blank.
• When 'amount applied for: NA/NP flag' equals 900, 'amount applied for' must not be blank.

Validation ID: E2008

• amount_approved.conditional_field_conflict
• When 'action taken' does not equal 1 (originated) or 2 (approved but not accepted), 'amount approved or originated' must be blank.
• When 'action taken' equals 1 or 2, 'amount approved or originated' must not be blank.

Validation ID: E2009

• action_taken_date.date_value_conflict
• The date indicated by 'action taken date' must occur on or after 'application date'.

Validation ID: E2011

• denial_reasons.enum_value_conflict
• When 'action taken' equals 3 (denied), 'denial reason(s)' must not contain 999 (not applicable).
• When 'action taken' does not equal 3, 'denial reason(s)' must equal 999.

Validation ID: E2012

• denial_reasons_ff.conditional_field_conflict
• When 'denial reason(s)' does not contain 977 (other), field 'free-form text field for other denial reason(s)' must be blank.
• When 'denial reason(s)' contains 977, 'free-form text field for other denial reason(s)' must not be blank.

Validation ID: E2014

• pricing_all.conditional_fieldset_conflict
• pricing_origination_charge

When 'action taken' equals 3 (denied), 4 (withdrawn by applicant), or 5 (incomplete), the following fields must all equal 999 (not applicable):

• 'Interest rate type'
• 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing: NA flag'
• 'Prepayment penalty could be imposed'
• 'Prepayment penalty exists'

And the following fields must all be blank:

• 'Total origination charges'
• 'Amount of total broker fees'
• 'Initial annual charges'

Validation ID: E2015

• pricing_charges.conditional_fieldset_conflict

When 'action taken' equals 1 (originated) or 2 (approved but not accepted), the following fields all must not be blank:

And the following fields must not equal 999 (not applicable):

Validation ID: E2016

• pricing_init_rate_period.conditional_field_conflict
• When 'interest rate type' does not equal 3 (initial rate period > 12 months, adjustable interest), 4 (initial rate period > 12 months, fixed interest), 5 (initial rate period <= 12 months, adjustable interest), or 6 (initial rate period <= 12 months, fixed interest), 'initial rate period' must be blank.
• When 'interest rate type' equals 3, 4, 5, or 6, 'initial rate period' must not be blank

Validation ID: E2017

• pricing_fixed_rate.conditional_field_conflict
• When 'interest rate type' does not equal 2 (fixed interest rate, no initial rate period), 4 (initial rate period > 12 months, fixed interest rate), or 6 (initial rate period <= 12 months, fixed interest rate), 'fixed rate: interest rate' must be blank.
• When 'interest rate type' equals 2, 4, or 6, 'fixed rate: interest rate' must not be blank.

Validation ID: E2018

• pricing_adj_margin.conditional_field_conflict
• When 'interest rate type' does not equal 1 (adjustable interest rate, no initial rate period), 3 (initial rate period > 12 months, adjustable interest rate), or 5 (initial rate period <= 12 months, adjustable interest rate), 'adjustable rate transaction: margin' must be blank.
• When 'interest rate type' equals 1, 3, or 5, 'adjustable rate transaction: margin' must not be blank.

Validation ID: E2019

• pricing_adj_index_name.enum_value_conflict
• When 'interest rate type' does not equal 1 (adjustable interest rate, no initial rate period), 3 (initial rate period > 12 months, adjustable interest rate), or 5 (initial rate period <= 12 months, adjustable interest rate), 'adjustable rate transaction: index name' must equal 999.
• When 'interest rate type' equals 1, 3, or 5, 'adjustable rate transaction: index name' must not equal 999.

Validation ID: E2020

• pricing_adj_index_name_ff.conditional_field_conflict
• pricing_adj_index_name;
• When 'adjustable rate transaction: index name' does not equal 977 (other), 'adjustable rate transaction: index name: other' must be blank
• When 'adjustable rate transaction: index name' equals 977, 'adjustable rate transaction: index name: other' must not be blank.

Validation ID: E2021

• pricing_adj_index_value.conditional_field_conflict
• When 'interest rate type' does not equal 1 (adjustable interest rate, no initial rate period), or 3 (initial rate period > 12 months, adjustable interest rate), 'adjustable rate transaction: index value' must be blank.
• When 'interest rate type' equals 1 or 3, 'adjustable rate transaction: index value' must not be blank.

Validation ID: E2022

• pricing_mca_addcost_flag.enum_value_conflict
• When 'credit product' does not equal 7 (merchant cash advance), 8 (other sales-based financing transaction) or 977 (other), 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing: NA flag' must be 999 (not applicable).

Validation ID: E2023

• pricing_mca_addcost.conditional_field_conflict
• When 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing: NA flag' does not equal 900 (applicable), 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing' must be blank.
• When 'MCA/sales-based: additional cost for merchant cash advances or other sales-based financing: NA flag' equals 900, ‘MCA/sales-based: additional cost for merchant cash advances or other sales-based financing’ must not be blank.

Validation ID: E2024

• census_tract_number.conditional_field_conflict
• When 'census tract: type of address' equals 988 (not provided by applicant and otherwise undetermined), ‘census tract: tract number’ must be blank.
• When 'census tract: type of address' equals 1 (address or location where the loan proceeds will principally be applied), 2 (address or location of borrower’s main office or headquarters), or 3 (another address or location associated with the applicant), 'census tract: tract number' must not be blank.

Validation ID: E2025

• gross_annual_revenue.conditional_field_conflict
• When 'gross annual revenue: NP flag' does not equal 900 (reported), 'gross annual revenue' must be blank.
• When 'gross annual revenue: NP flag' equals 900, 'gross annual revenue' must not be blank.

Validation ID: E2026

• naics_code.conditional_field_conflict
• When 'North American Industry Classification System (NAICS) code: NP flag' does not equal 900 (reported), 'North American Industry Classification System (NAICS) code' must be blank.
• When 'North American Industry Classification System (NAICS) code: NP flag' equals 900, 'North American Industry Classification System (NAICS) code' must not be blank.

Validation ID: E2027

• time_in_business.conditional_field_conflict
• When 'time in business: type of response' does not equal 1 (the number of years an applicant has been in business is collected or obtained by the financial institution), 'time in business' must be blank.
• When 'time in business: type of response' equals 1, 'time in business' must not be blank.

Validation ID: E2028

• num_principal_owners.conditional_field_conflict
• When 'number of principal owners: NP flag' does not equal 900 (reported), 'number of principal owners' must be blank.
• When 'number of principal owners: NP flag' equals 900, 'number of principal owners' must not be blank.

Validation ID: E2040

• po_1_ethnicity_ff.conditional_field_conflict
• When 'ethnicity of principal owner 1' does not contain 977 (the applicant responded in the free-form text field), 'ethnicity of principal owner 1: free-form text field for other Hispanic or Latino' must be blank.
• When 'ethnicity of principal owner 1' contains 977, 'ethnicity of principal owner 1: free-form text field for other Hispanic or Latino' must not be blank.

Validation ID: E2041

• po_2_ethnicity_ff.conditional_field_conflict
• When 'ethnicity of principal owner 2' does not contain 977 (the applicant responded in the free-form text field), 'ethnicity of principal owner 2: free-form text field for other Hispanic or Latino' must be blank.
• When 'ethnicity of principal owner 2' contains 977, 'ethnicity of principal owner 2: free-form text field for other Hispanic or Latino' must not be blank.

Validation ID: E2042

• po_3_ethnicity_ff.conditional_field_conflict
• When 'ethnicity of principal owner 3' does not contain 977 (the applicant responded in the free-form text field), 'ethnicity of principal owner 3: free-form text field for other Hispanic or Latino' must be blank.
• When 'ethnicity of principal owner 3' contains 977, 'ethnicity of principal owner 3: free-form text field for other Hispanic or Latino' must not be blank.

Validation ID: E2043

• po_4_ethnicity_ff.conditional_field_conflict
• When 'ethnicity of principal owner 4' does not contain 977 (the applicant responded in the free-form text field), 'ethnicity of principal owner 4: free-form text field for other Hispanic or Latino' must be blank.
• When 'ethnicity of principal owner 4' contains 977, 'ethnicity of principal owner 4: free-form text field for other Hispanic or Latino' must not be blank.

Validation ID: E2060

• po_1_race_anai_ff.conditional_field_conflict
• When 'race of principal owner 1' does not contain 971 (the applicant responded in the free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe), 'race of principal owner 1: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must be blank.
• When 'race of principal owner 1' contains 971, 'race of principal owner 1: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not be blank.

Validation ID: E2061

• po_2_race_anai_ff.conditional_field_conflict
• When 'race of principal owner 2' does not contain 971 (the applicant responded in the free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe), 'race of principal owner 2: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must be blank.
• When 'race of principal owner 2' contains 971, 'race of principal owner 2: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not be blank.

Validation ID: E2062

• po_3_race_anai_ff.conditional_field_conflict
• When 'race of principal owner 3' does not contain 971 (the applicant responded in the free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe), 'race of principal owner 3: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must be blank.
• When 'race of principal owner 3' contains 971, 'race of principal owner 3: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not be blank.

Validation ID: E2063

• po_4_race_anai_ff.conditional_field_conflict
• When 'race of principal owner 4' does not contain 971 (the applicant responded in the free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe), 'race of principal owner 4: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must be blank.
• When 'race of principal owner 4' contains 971, 'race of principal owner 4: free-form text field for American Indian or Alaska Native Enrolled or Principal Tribe' must not be blank.

Validation ID: E2080

• po_1_race_asian_ff.conditional_field_conflict
• When 'race of principal owner 1' does not contain 972 (the applicant responded in the free-form text field for other Asian race), 'race of principal owner 1: free-form text field for other Asian' must be blank.
• When 'race of principal owner 1' contains 972, 'race of principal owner 1: free-form text field for other Asian' must not be blank.

Validation ID: E2081

• po_2_race_asian_ff.conditional_field_conflict
• When 'race of principal owner 2' does not contain 972 (the applicant responded in the free-form text field for other Asian race), 'race of principal owner 2: free-form text field for other Asian' must be blank.
• When 'race of principal owner 2' contains 972, 'race of principal owner 2: free-form text field for other Asian' must not be blank.

Validation ID: E2082

• po_3_race_asian_ff.conditional_field_conflict
• When 'race of principal owner 3' does not contain 972 (the applicant responded in the free-form text field for other Asian race), 'race of principal owner 3: free-form text field for other Asian' must be blank.
• When 'race of principal owner 3' contains 972, 'race of principal owner 3: free-form text field for other Asian' must not be blank.

Validation ID: E2083

• po_4_race_asian_ff.conditional_field_conflict
• When 'race of principal owner 4' does not contain 972 (the applicant responded in the free-form text field for other Asian race), 'race of principal owner 4: free-form text field for other Asian' must be blank.
• When 'race of principal owner 4' contains 972, 'race of principal owner 4: free-form text field for other Asian' must not be blank.

Validation ID: E2100

• po_1_race_baa_ff.conditional_field_conflict
• When 'race of principal owner 1' does not contain 973 (the applicant responded in the free-form text field for other Black or African race), 'race of principal owner 1: free-form text field for other Black or African American' must be blank.
• When 'race of principal owner 1' contains 973, 'race of principal owner 1: free-form text field for other Black or African American' must not be blank.

Validation ID: E2101

• po_2_race_baa_ff.conditional_field_conflict
• When 'race of principal owner 2' does not contain 973 (the applicant responded in the free-form text field for other Black or African race), 'race of principal owner 2: free-form text field for other Black or African American' must be blank.
• When 'race of principal owner 2' contains 973, 'race of principal owner 2: free-form text field for other Black or African American' must not be blank.

Validation ID: E2102

• po_3_race_baa_ff.conditional_field_conflict
• When 'race of principal owner 3' does not contain 973 (the applicant responded in the free-form text field for other Black or African race), 'race of principal owner 3: free-form text field for other Black or African American' must be blank.
• When 'race of principal owner 3' contains 973, 'race of principal owner 3: free-form text field for other Black or African American' must not be blank.

Validation ID: E2103

• po_4_race_baa_ff.conditional_field_conflict
• When 'race of principal owner 4' does not contain 973 (the applicant responded in the free-form text field for other Black or African race), 'race of principal owner 4: free-form text field for other Black or African American' must be blank.
• When 'race of principal owner 4' contains 973, 'race of principal owner 4: free-form text field for other Black or African American' must not be blank.

Validation ID: E2120

• po_1_race_pi_ff.conditional_field_conflict
• When 'race of principal owner 1' does not contain 974 (the applicant responded in the free-form text field for other Pacific Islander race), 'race of principal owner 1: free-form text field for other Pacific Islander race' must be blank.
• When 'race of principal owner 1' contains 974, 'race of principal owner 1: free-form text field for other Pacific Islander race' must not be blank.

Validation ID: E2121

• po_2_race_pi_ff.conditional_field_conflict
• When 'race of principal owner 2' does not contain 974 (the applicant responded in the free-form text field for other Pacific Islander race), 'race of principal owner 2: free-form text field for other Pacific Islander race' must be blank.
• When 'race of principal owner 2' contains 974, 'race of principal owner 2: free-form text field for other Pacific Islander race' must not be blank.

Validation ID: E2122

• po_3_race_pi_ff.conditional_field_conflict
• When 'race of principal owner 3' does not contain 974 (the applicant responded in the free-form text field for other Pacific Islander race), 'race of principal owner 3: free-form text field for other Pacific Islander race' must be blank.
• When 'race of principal owner 3' contains 974, 'race of principal owner 3: free-form text field for other Pacific Islander race' must not be blank.

Validation ID: E2123

• po_4_race_pi_ff.conditional_field_conflict
• When 'race of principal owner 4' does not contain 974 (the applicant responded in the free-form text field for other Pacific Islander race), 'race of principal owner 4: free-form text field for other Pacific Islander race' must be blank.
• When 'race of principal owner 4' contains 974, 'race of principal owner 4: free-form text field for other Pacific Islander race' must not be blank.

Validation ID: E2140

• po_1_gender_ff.conditional_field_conflict
• When 'sex/gender of principal owner 1: NP flag' does not equal 1 (the applicant responded in the free-form text field), 'sex/gender of principal owner 1: free-form text field for self-identified sex/gender' must be blank.
• When 'sex/gender of principal owner 1: NP flag' equals 1, 'sex/gender of principal owner 1: free-form text field for self-identified sex/gender' must not be blank.

Validation ID: E2141

• po_2_gender_ff.conditional_field_conflict
• When 'sex/gender of principal owner 2: NP flag' does not equal 1 (the applicant responded in the free-form text field), 'sex/gender of principal owner 2: free-form text field for self-identified sex/gender' must be blank.
• When 'sex/gender of principal owner 2: NP flag' equals 1, 'sex/gender of principal owner 2: free-form text field for self-identified sex/gender' must not be blank.

Validation ID: E2142

• po_3_gender_ff.conditional_field_conflict
• When 'sex/gender of principal owner 3: NP flag' does not equal 1 (the applicant responded in the free-form text field), 'sex/gender of principal owner 3: free-form text field for self-identified sex/gender' must be blank.
• When 'sex/gender of principal owner 3: NP flag' equals 1, 'sex/gender of principal owner 3: free-form text field for self-identified sex/gender' must not be blank.

Validation ID: E2143

• po_4_gender_ff.conditional_field_conflict
• When 'sex/gender of principal owner 4: NP flag' does not equal 1 (the applicant responded in the free-form text field), 'sex/gender of principal owner 4: free-form text field for self-identified sex/gender' must be blank.
• When 'sex/gender of principal owner 4: NP flag' equals 1, 'sex/gender of principal owner 4: free-form text field for self-identified sex/gender' must not be blank.

4.3. Register-level errors

Validation id: e3000.

• uid.duplicates_in_dataset
• Any 'unique identifier' may not be used in more than one record within a small business lending application register.

4.4. Single-field warnings

Validation id: w0003.

• uid.invalid_uid_lei
• The first 20 characters of the 'unique identifier' should match the Legal Entity Identifier (LEI) for the financial institution.

Validation ID: W0122

• ct_guarantee.multi_value_field_restriction
• When 'type of guarantee' contains 999 (no guarantee), 'type of guarantee' should not contain more than one value.

Validation ID: W0123

• ct_guarantee.duplicates_in_field
• 'Type of guarantee' should not contain duplicated values.

Validation ID: W0182

• ct_loan_term.unreasonable_numeric_value
• When present, 'loan term' should be less than 1200 (100 years).

Validation ID: W0202

• credit_purpose.multi_value_field_restriction
• When 'credit purpose' contains 988 (not provided by applicant and otherwise undetermined) or 999 (not applicable), 'credit purpose' should not contain more than one value.

Validation ID: W0203

• credit_purpose.duplicates_in_field
• 'Credit purpose' should not contain duplicated values.

Validation ID: W0340

• denial_reasons.multi_value_field_restriction
• When 'denial reason(s)' contains 999 (not applicable), 'denial reason(s)' should not contain more than one value.

Validation ID: W0341

• denial_reasons.duplicates_in_field
• 'Denial reason(s)' should not contain duplicated values.

Validation ID: W0420

• pricing_fixed_rate.unreasonable_numeric_value
• When present, 'fixed rate: interest rate' should generally be greater than 0.1.

Validation ID: W0441

• pricing_adj_margin.unreasonable_numeric_value
• When present, 'adjustable rate transaction: margin' should generally be greater than 0.1.

Validation ID: W0680

• census_tract_number.invalid_geoid
• When present, 'census tract: tract number' should be a valid census tract GEOID as defined by the U.S. Census Bureau.

Validation ID: W0762

• naics_code.invalid_naics_value
• When present, 'North American Industry Classification System (NAICS) code' should be a valid NAICS code.

Validation ID: W0842

• business_ownership_status.duplicates_in_field
• 'Business ownership status' should not contain duplicated values.

Validation ID: W0843

• business_ownership_status.multi_value_field_restriction
• When 'business ownership status' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'business ownership status' should not contain more than one value.

Validation ID: W0901

• po_1_ethnicity.duplicates_in_field
• 'Ethnicity of principal owner 1' should not contain duplicated values.

Validation ID: W0902

• po_1_ethnicity.multi_value_field_restriction
• When 'ethnicity of principal owner 1' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'ethnicity of principal owner 1' should not contain more than one value.

Validation ID: W0941

• po_1_race.duplicates_in_field
• 'Race of principal owner 1' should not contain duplicated values.

Validation ID: W0942

• po_1_race.multi_value_field_restriction
• When 'race of principal owner 1' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'race of principal owner 1' should not contain more than one value.

Validation ID: W1081

• po_2_ethnicity.duplicates_in_field
• 'Ethnicity of principal owner 2' should not contain duplicated values.

Validation ID: W1082

• po_2_ethnicity.multi_value_field_restriction
• When 'ethnicity of principal owner 2' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'ethnicity of principal owner 2' should not contain more than one value.

Validation ID: W1121

• po_2_race.duplicates_in_field
• 'Race of principal owner 2' should not contain duplicated values.

Validation ID: W1122

• po_2_race.multi_value_field_restriction
• When 'race of principal owner 2' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'race of principal owner 2' should not contain more than one value.

Validation ID: W1261

• po_3_ethnicity.duplicates_in_field
• 'Ethnicity of principal owner 3' should not contain duplicated values.

Validation ID: W1262

• po_3_ethnicity.multi_value_field_restriction
• When 'ethnicity of principal owner 3' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'ethnicity of principal owner 3' should not contain more than one value.

Validation ID: W1301

• po_3_race.duplicates_in_field
• 'Race of principal owner 3' should not contain duplicated values.

Validation ID: W1302

• po_3_race.multi_value_field_restriction
• When 'race of principal owner 3' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'race of principal owner 3' should not contain more than one value.

Validation ID: W1441

• po_4_ethnicity.duplicates_in_field
• 'Ethnicity of principal owner 4' should not contain duplicated values.

Validation ID: W1442

• po_4_ethnicity.multi_value_field_restriction
• When 'ethnicity of principal owner 4' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'ethnicity of principal owner 4' should not contain more than one value.

Validation ID: W1481

• po_4_race.duplicates_in_field
• 'Race of principal owner 4' should not contain duplicated values.

Validation ID: W1482

• po_4_race.multi_value_field_restriction
• When 'race of principal owner 4' contains 966 (the applicant responded that they did not wish to provide this information) or 988 (not provided by applicant), 'race of principal owner 4' should not contain more than one value.

4.5. Multi-field warnings

Validation id: w2002.

• ct_guarantee_ff.multi_invalid_number_of_values
• 'Type of guarantee' and 'free-form text field for other guarantee' combined should not contain more than five values. Code 977 (other), within 'type of guarantee', does not count toward the maximum number of values for the purpose of this validation check.

Validation ID: W2006

• credit_purpose_ff.multi_invalid_number_of_values
• 'Credit purpose' and 'free-form text field for other credit purpose' combined should not contain more than three values. Code 977 (other), within 'credit purpose', does not count toward the maximum number of values for the purpose of this validation check.

Validation ID: W2010

• action_taken_date.unreasonable_date_value
• The date indicated by 'application date' should generally be less than two years (730 days) before 'action taken date'.

Validation ID: W2013

• denial_reasons_ff.multi_invalid_number_of_values
• 'Denial reason(s)' and 'free-form text field for other denial reason(s)' combined should not contain more than four values. Code 977 (other), within 'Denial reason(s)', does not count toward the maximum number of values for the purpose of this validation check.

Validation ID: W2035

• po_demographics_0.conditional_fieldset_conflict
• When 'number of principal owners' equals 0 or is blank, demographic fields for principal owners 1, 2, 3, and 4 should be blank.

Validation ID: W2036

• po_demographics_1.conditional_fieldset_conflict
• When 'number of principal owners' equals 1, 'ethnicity of principal owner 1', 'race of principal owner 1', and 'sex/gender of principal owner 1: NP flag' should not be blank.
• Demographic fields for principal owners 2, 3, and 4 should be blank.

Validation ID: W2037

• po_demographics_2.conditional_fieldset_conflict
• When 'number of principal owners' equals 2, 'ethnicity of principal owner 1 and 2', 'race of principal owner 1 and 2', and 'sex/gender of principal owner 1 and 2: NP flag' should not be blank.
• Demographic fields for principal owners 3 and 4 should be blank.

Validation ID: W2038

• po_demographics_3.conditional_fieldset_conflict
• When 'number of principal owners' equals 3, 'ethnicity of principal owner 1, 2, and 3', 'race of principal owner 1, 2, and 3', and 'sex/gender of principal owner 1, 2, and 3: NP flag' should not be blank.
• Demographic fields for principal owner 4 should be blank.

Validation ID: W2039

• po_demographics_4.conditional_fieldset_conflict
• When 'number of principal owners' equals 4, 'ethnicity of principal owner 1, 2, 3, and 4', 'race of principal owner 1, 2, 3, and 4', and 'sex/gender of principal owner 1, 2, 3, and 4: NP flag' should not be blank.

5. Where to get help

Resources to help industry understand and comply with the small business lending rule are available on the CFPB’s website. Learn about complying with the small business lending rule.

You may also sign up for an email distribution list that the CFPB will use to announce future updates and additional resources as they become available. If you have a specific regulatory interpretation question about the small business lending rule after reviewing these resources, you can submit the question to the CFPB on its website.

6. Paperwork Reduction Act

According to the Paperwork Reduction Act of 1995, an agency may not conduct or sponsor, and, notwithstanding any other provision of law, a person is not required to respond to a collection of information unless it displays a valid OMB control number. The OMB control number for this collection is 3170-0013. It expires on November 30, 2025. The obligation to respond to this collection of information is mandatory under section 704B of the Equal Credit Opportunity Act, 15 U.S.C. 1691c-2, as implemented by Consumer Financial Protection Bureau’s Regulation B, 12 CFR part 1002. Comments regarding this collection of information, including the estimated response time, suggestions for improving the usefulness of the information, or suggestions for reducing the burden to respond to this collection should be submitted to the Bureau of Consumer Financial Protection (Attention: PRA Office), 1700 G Street NW, Washington, DC 20552, or by email to [email protected] .

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• Main content

Have Stress Tests Impacted Small-Business Lending?

• Yuliya Demyanyk

The Federal Reserve conducts stress tests of the largest bank holding companies to ensure that the banking system has sufficient capital to stay financially sound in the event of worsening economic conditions. Some groups have raised concerns that the stress tests will reduce lending to small businesses. This article describes recent research investigating the impact of the stress tests on small-business lending. It finds that the banks that are most affected by stress tests have reduced their small-business credit, but aggregate credit to small businesses has not fallen.

The 2008 financial crisis led to dramatic changes in the regulation and supervision of financial institutions, including one new requirement for a select group of large bank holding companies (BHCs): the annual stress test. Stress testing aims to ensure that in the event of worsening economic conditions, banks will have sufficient capital to absorb losses and be able to continue supplying credit to the economy.

Stress tests provide an estimate of how much capital a BHC might lose during a severe economic downturn, and that estimate is then translated into a forecast of the regulatory capital ratios the BHC would be required to hold under various economic scenarios. If the stress tests project large shortfalls in a BHC’s capital, the banks owned by that BHC are likely to be given the incentive to respond by reducing the risks in their current loan portfolios or by improving their current capital ratios by, for example, reducing planned dividend distributions or share repurchases.

Because lending to small businesses may be considered riskier than lending to large businesses, concerns have been raised that the stress tests might induce banks to reduce their lending to this market. 1  The research on this topic suggests that banks facing regulatory capital constraints cut their lending, and stress tests create a direct link from bank lending risk to capital. 2

This Commentary discusses new research that assesses the impact that stress tests have had on small-business lending (Cortés et al., forthcoming). We show that the banks most affected by stress tests reallocate small-business credit away from riskier markets to safer ones. They also raise interest rates on small-business loans in the markets in which they continue to lend. Loan quantities fall most in high-risk markets where stress-tested banks own no branches, and prices rise mainly where they have branches. These facts suggest that due to a stress-test-projected shortfall in regulatory capital, banks increase small-business loan prices in markets in which the banks have local knowledge and to exit markets in which they do not. Stress tests do not, however, reduce aggregate small-business credit. In geographies where small firms formerly relied on stress-tested lenders, small firms see no reduction in credit and likely find it from other local lenders.

A Brief History of Stress Testing

The first stress test in the United States was conducted by the Federal Reserve in 2009 during the financial crisis. Called the Supervisory Capital Assessment Program (SCAP), it was introduced to ensure that banks had sufficient capital coming out of the crisis to absorb losses in the event of another crisis. After the SCAP concluded, the Federal Reserve decided to continue stress testing on an annual basis, renaming the program the Comprehensive Capital Analysis and Review (CCAR). CCAR began in 2011 with the same set of large BHCs as the SCAP, those with total assets in excess of $100 billion, but in 2012, the set was expanded to include all 32 of the BHCs with assets above $50 billion. Starting in 2013, the Federal Reserve began implementing dual stress tests, one based on the CCAR process and the other based on compliance with the Dodd-Frank Act, called DFAST. The key difference between the two tests is that under CCAR, each BHC provides a proposed capital distribution plan that regulators incorporate into the stress test; under DFAST, regulators assume the bank’s capital distribution is held at its current level. 3  The tests were originally disclosed in March of each year, but in 2016, the report date for the stress-test disclosure was moved to late June. 4

Both CCAR and DFAST aim to evaluate what happens to each BHC’s capital under three possible economic scenarios—“baseline,” “adverse,” and “severely adverse”—nine quarters into the future. The scenarios reflect possible paths for aggregate economic variables. In 2017, the variables included “six measures of economic activity and prices: percent changes (at an annual rate) in real and nominal gross domestic product (GDP); the unemployment rate of the civilian noninstitutional population aged 16 years and over; percent changes (at an annual rate) in real and nominal disposable personal income; and the percent change (at an annual rate) in the consumer price index.” 5  The stress tests map the effects of these variables’ hypothetical values on the capital ratios of each BHC over the course of the forecast.

By focusing on measures of economic activity and prices, the possible scenarios focus on aggregate rather than idiosyncratic risks to banks. This approach helps minimize the macroprudential risk of banks’ capital becoming collectively constrained during broad economic downturns. However, data on individual BHC positions and exposures to various risk factors are also incorporated into the stress tests. Thus, the results of the stress tests are based on common scenarios and a common model (i.e., the one developed by the Federal Reserve), but they account for differences in asset composition. 6  The results are watched closely, not only by regulators, but also by bank managers, analysts, and investors, as they might lead to forced reductions in a BHC’s planned capital distributions, along with other operating changes, if the simulated decline in capital is sufficiently large. Stress tests have been widely adopted by regulatory authorities outside the United States, such as the Bank of England and the European Central Bank.

Research Methodology

In a recent study, my coauthors and I evaluate the effects of the stress tests on small-business lending during the 2012–2015 period (Cortés et al., forthcoming). We begin by developing several measures of the impact of the stress-test results on each stress-tested BHC in each year, that is, the degree to which capital ratios are projected to fall short of requirements in the various scenarios. We call these measures of “stress-test exposure.” Using these measures, we investigate whether the BHCs more affected by stress tests cut the supply of small-business lending more than those less affected.

Generally, there are two components to a cut in lending supply: One is a drop in the number of loans originated and the other is an increase in loan prices. Thus, we consider quantity and price separately. Finally, in addition to evaluating whether the stress tests reduced stress-tested banks’ lending to small businesses, we evaluate whether stress tests affected the overall supply of small-business loans.

Measuring Stress-Test Exposure

The Federal Reserve discloses the results of the stress tests for three capital ratios: the Tier 1 capital ratio, the total risk-based capital ratio, and the Tier 1 leverage ratio. The results reported are the projected values of these ratios (“stressed ratios”) for each BHC under the three scenarios over the forward-looking nine-quarter planning horizon in each annual test cycle. 7  Stressed capital ratios thus capture changes in the value of BHC portfolios under stress. We use these stressed ratios to create our measure of stress-test exposure. For each BHC, we take the difference between each of the three stressed ratios and its respective regulatory threshold (6 percent for the Tier 1 ratio; 8 percent for the total risk-based capital ratio; and 4 percent for the Tier 1 leverage ratio) and then use the smallest difference of the three as that BHC’s degree of stress-test exposure:

Stress-test exposure = minimum (stressed Tier 1 capital − 6%; stressed total risk-based capital − 8%; stressed leverage ratio − 4%)

BHCs whose specific portfolios have the greatest downside risk would have the most stress-test exposure and would be closest to one of the regulatory capital ratio thresholds. Banks owned by these BHCs likely face pressure from the regulators either to reduce risk or improve their current capital ratios (for example, by reducing planned dividend distributions or share repurchases).

Finally, we identify the banks that are owned by each of the 32 CCAR stress-tested BHCs. We use Call Reports for this information. We assume measures taken by BHCs to address the results of the stress test will manifest in the behavior of their subsidiary banks.

The Impact of Stress-Test Exposure on Loan Quantities

To capture the response of small-business loan quantities to stress-test exposure, we use CRA loan origination data from 2012–2015, collected by the Federal Financial Institutions Examination Council at the subsidiary bank level. CRA reports include data on loans with commitment amounts below $1 million that are originated by financial institutions with more than $1 billion in assets. CRA data provide us with a complete record of new lending quantities by subsidiary banks of the stress-tested BHCs at the county–year level. We use these data to build the annual growth rate of new loan originations under $1 million, a threshold we interpret as loans to small businesses.

We merge the annual CRA data with stress-test exposure data based on the identity of a subsidiary bank’s parent BHC. Since the stress-test results were published in March during our study period, we operate under the assumption that the majority of the effect from the stress tests on small-business lending manifests within the next nine months of the year of the disclosure. In line with this assumption, we match, for example, CRA loan growth from December 2013 to December 2014 to the stress-test results reported in March 2014. 8

We expect that the subsidiaries of BHCs with higher stress-test exposure will reduce their lending to small businesses. One can argue, however, that banks that are more inclined to take risks would both grow their loan portfolios faster and experience higher stress-test exposure, thus biasing up the direct effect of stress-test exposure on loan growth. To mitigate this concern, instead of evaluating the direct effect of stress-test exposure on CRA loan growth, we focus on interactions between stress-test exposure and risk and between stress-test exposure and access to soft information (measured by branch proximity to borrowers).

If a bank attempts to reduce its loan-risk exposure because of stress-test results, we should observe steeper reductions of small-business loan quantities in riskier markets. In addition, this reduction in the quantity of loans should be most pronounced in markets without branches. Without the close relationships to customers that branches enable, banks are less able to “price in” the higher capital burden from stress-test exposure in higher interest rates and instead they exit the market.

Since CRA data do not provide information about individual borrower risk, we build a county-level risk measure as a proxy measure for borrower risk. Using county employment data, we construct the employment beta, which captures the sensitivity of a county’s employment growth to changes in national employment growth. With this measure, counties with larger employment betas are considered riskier than those with lower betas.

The results suggest that banks with higher stress-test exposure are more likely to exit risky markets (counties). The estimates reported in panel A of 1 suggest that in response to a one standard deviation increase in stress-test exposure (=1.4 percent), markets in the top quartile of the employment beta distribution (beta = 1.36) would see a 2.5 percent greater decline in small-business loan originations than those in the bottom beta quartile (beta = 0.96).

We augment this analysis to evaluate whether the effect of stress testing on lending quantities differs across markets in which banks have and do not have an informational advantage through a branch presence. Panel B of figure 1 demonstrates small-business lending sensitivity to stress-test exposure in counties in which subsidiary banks have at least one branch; panel C demonstrates the results for counties in which lenders do not have a branch. We find that the effect of stress-test exposure on loan quantities is pronounced in markets in which banks have no branches, yet the effect is virtually nonexistent in markets in which banks have branches.

The evidence provided here offers a direct link between small-business loan originations and stress-test exposure. The decline in supplied loan quantities is more pronounced in riskier markets and in markets in which banks lack local knowledge because of the absence of a branch presence.

The Impact of the Stress-Test Exposure on Loan Prices

To analyze the impact of stress tests on loan prices, we use data from the Survey of Terms of Business Lending (STBL) covering a 2012:Q2–2017:Q2 sample. The Federal Reserve instituted the STBL to obtain timely information on the business-lending environment in the United States. The STBL collects data on loans originated by a random sample of banks during a full business week every three months (in February, May, August, and November). The selection of banks is conducted in a way that creates a representative sample of commercial and industrial loans. Consequently, large banks are more likely to be surveyed. The STBL data cover banks owned by 26 of the 32 stress-tested BHCs and provide detailed loan characteristics including loan size, the nominal interest rate, maturity, whether or not the loan comes with a prepayment penalty, collateral status, the state of the borrower, and so on. In addition to these characteristics, the STBL reports the lender’s internal risk rating for each loan. The rating ranges from 1 to 4, with 1 representing loans with the lowest risk level and 4 representing those with the highest risk level. 9  Capturing loan risk helps alleviate the alternative explanations stemming from the bank risk preferences discussed earlier.

We map these quarterly STBL data to the annual BHC-level stress-test exposure data on a rolling basis based on the identity of a bank’s parent BHC. Since we want stress-test exposure to be predetermined with respect to our outcomes, we map each stress-test result into the next four STBL quarterly surveys. For example, we map the March 2013 values of stress-test exposures into STBL data from May 2013, August 2013, November 2013, and February 2014. Since 2016 stress-test results are reported in June, we map 2016 stress-test exposure measures to STBL data from August 2016, November 2016, February 2017, and May 2017. We then merge the STBL data with BHC financial characteristics by using Call Reports as of the last date available prior to the STBL loan cohort date. For example, we merge the STBL survey taken in August of 2013 with the (last available prior to August) June 2013 Call Report data.

Using a statistical model, we evaluate the impact of stress-test exposure on loan interest rates. Because factors other than stress-test results may explain why some loans carry higher or lower interest rates, we include in our model a loan risk level, other loan characteristics, BHC characteristics, and an indicator of whether a bank is local in a state, that is, if a bank has branches in a locality in which it lends. We also include a measure capturing local market demand (in statistical terms, state-quarter fixed effects). Based on the estimated results, we find that higher stress-test exposure corresponds to higher interest rates. In addition, stress tests affect loan pricing more in markets where banks have a local branch presence. Specifically, a one standard deviation higher stress-test exposure is associated with an increase in interest rates of 38 basis points in markets in which banks have a branch presence and with an increase of only 14 basis points in markets in which they do not have branches. The results are consistent with the notion that because of their informational advantage, banks with local knowledge are more able to increase prices without borrowers’ switching to other lenders.

We then split our analysis by borrower risk. Based on the estimated results, prices of low-risk loans do not change reliably with a bank’s stress-test exposure. Interest rates on medium-risk and high-risk loans, however, do increase robustly with higher stress-test exposure. The effect on rates in these two categories is also larger in areas in which banks have a local branch presence. Moreover, the impact of stress-test exposure on loan rates is the greatest for the high-risk loans in local markets.

Overall, banks more affected by stress tests increase interest rates on risky local loans more than banks that are less affected. In contrast, interest rates on low-risk loans do not change. These findings are consistent with the CRA-based evidence on quantities that suggests stress tests induce a shift away from riskier nonlocal markets toward local markets in which banks have an informational advantage.

The Impact of Stress-Test Exposure on Aggregate Small-Business Lending

Our results indicate that individual banks’ credit supply was affected by their exposure to stress tests. But this leaves the question of whether the stress tests constrain overall credit production. Perhaps lenders that were not affected by stress tests step in to lend to the displaced borrowers formerly served by stress-tested banks. And, as we have discussed, local stress-tested banks raise prices on risky loans and thus may continue to provide credit. To address this question, we revisit the CRA quantity data, but we now evaluate aggregate annual origination volumes in different markets (counties).

In our empirical model, we evaluate growth rates in small-business loan originations at the county level in each year. Given that local and nonlocal banks respond differently to stress-test exposure, we construct two county-level measures of exposure. The first, local banks’ stress-test exposure, equals the average exposure for all banks with branches in each county and year, weighted by banks’ local loan share in 2010 (before the first year of our sample). The second county-level exposure measure, nonlocal banks’ stress-test exposure, is built similarly and equals the average for banks without branches in each county and year and is also weighted by banks’ local loan share in 2010. If stress tests lead to tightening of aggregate small-business credit, then the estimated impact of the tests would be negative because an increase in stress-test exposure at a county level would be associated with credit contraction. Furthermore, since we find that nonlocal banks cut credit more than local ones, we might expect the results for the nonlocal measure to be larger in magnitude than for the local measure. To capture overall economic conditions at the county level, we include county and year fixed effects, along with possible local time-varying drivers of loan demand (housing price growth, employment growth, and income growth). Based on our results, we find no difference in aggregate credit origination across markets, regardless of local market reliance on small-business lending from local or nonlocal stress-tested banks.

One possible explanation for this result is that nontested (smaller) banks fill in the gap and lend to businesses that the stress-tested banks no longer serve. To test this conjecture, we examine the relationship between stress-test exposure and the share of loans originated by local banks unaffected by stress tests: banks with assets below $10 billion. 10  We empirically confirm that small, local banks increase their share when stress-tested banks are closer to binding capital requirements. These results, taken together with the results discussed earlier, suggest that small banks unaffected by stress testing, and perhaps nonbank lenders as well, substitute in for large, nonlocal banks in lending to small businesses.

Conclusions

Our results suggest that banks more affected by stress tests have reduced their supply of loans to small businesses, and this reduction has been concentrated among relatively riskier small-business borrowers and riskier markets. Loan quantities fall more in markets in which stress-tested banks do not own branches near borrowers, and prices rise predominantly where they do. These differential responses emphasize the importance of market structure and branch location in mediating the impact of capital requirements on credit supply. Aggregate credit, however, has not been adversely affected by stress tests. Instead, credit seems to be supplied by small, local lenders when large stress-tested banks exit those markets.

Our results suggest that stress tests qualitatively work as intended. We observe that tested lenders either reduce their exposure to risk or, when they don’t, they increase their compensation for bearing that risk. These changes would be efficiency-enhancing if large banks were taking on too much risk and extending too much credit prior to the financial crisis, as they would under theories of moral hazard incentives from deposit insurance and “too big to fail” expectations. Regulations that accurately tie loan risk to required capital can help alleviate these distortions. Stress tests may help with these objectives by moving small-business credit supply from large, nonlocal lenders toward smaller banks with more local knowledge.

The views authors express in Economic Commentary are theirs and not necessarily those of the Federal Reserve Bank of Cleveland or the Board of Governors of the Federal Reserve System. The series editor is Tasia Hane. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. This paper and its data are subject to revision; please visit clevelandfed.org  for updates.

• The Clearing House, an advocate for banks, points specifically to the stress tests as imposing unduly harsh (implicit) capital requirements on small-business loans and on residential mortgages (Covas, 2017a and 2017b). Return to 1
• A large academic literature on bank “capital crunches” documents that shocks to bank equity capital have large contractionary effects on the supply of lending (Bernanke, 1983; Bernanke, Lown, and Friedman, 1991; Kashyap and Stein, 1995; Kashyap and Stein, 2000; Houston, James, and Marcus, 1997; Peek and Rosengren, 1997; Peek and Rosengren, 2000; Campello, 2002; Calomiris and Mason, 2003; Calomiris and Wilson, 2004; Cetorelli and Goldberg, 2012; Cortés and Strahan, 2017). Return to 2
• There are other differences between the CCAR and DFAST; see the following documentation for more details: https://www.federalreserve.gov/newsevents/press/bcreg/dfast_2013_results_20130314.pdf. Return to 3
• For 2019, the Federal Reserve proposed more changes to the stress tests, “providing relief to less-complex firms from stress testing requirements and CCAR by effectively moving the firms to an extended stress test cycle for this year. The relief applies to firms generally with total consolidated assets between $100 billion and $250 billion.” https://www.federalreserve.gov/newsevents/pressreleases/bcreg20190205b.htm . We do not use the most recent data for our analysis. Return to 4
• 2017 Supervisory Scenarios for Annual Stress Tests Required under the Dodd-Frank Act Stress Testing Rules and the Capital Plan Rule: https://www.federalreserve.gov/newsevents/pressreleases/files/bcreg20170203a5.pdf. Return to 5
• Banks are required to create their own models of stress testing but neither the Federal Reserve’s nor the banks’ internal models are available to the public. Return to 6
• Our data for 2012 are taken from the Federal Reserve’s CCAR disclosure, but we use the series of results that do not include the bank’s capital distribution plan. Data from 2013–2016 are taken from the disclosure under the Dodd-Frank Act. In other words, our sample includes only the CCAR banks, but the measure of exposure is the one used for compliance with DFAST, which does not incorporate the bank’s capital distribution plan. Several other regulatory capital ratios are used in some of the stress-test cycles, but the three we use are the only ones available consistently across all cycles. Return to 7
• We limited the period of our analysis to 2012–2015 because the publication date of the results was moved in 2016 from March to June. At the time of our research, we had data through the end of 2016, but because CRA data are annual and the 2016 stress test results would not be released until June, lending adjustments made in response to the stress-test results were unlikely to be properly captured by 2016 annual data on small-business lending growth. Return to 8
• The risk rating in the raw data ranges from 0 to 5.     We exclude from consideration distressed loans (risk rating = 5) that do not reflect new originations. Furthermore, since controlling for risk is an important factor in our identification strategy, we exclude from consideration unrated loans (risk rating = 0). Return to 9
• We use the $10 billion cut-off to ensure the banks are not affected by any stress tests. In 2014, the stress-test process was expanded to banks with total assets between $10 billion and $50 billion under the Dodd-Frank Act. However, stress tests of banks with assets between $10 billion and $50 billion were not disclosed before 2016. Return to 10
• Bernanke, Ben S. 1983. “Nonmonetary Effects of the Financial Crisis in the Propagation of the Great Depression.”  American Economic Review , 73(3): 257–276.  http://www.jstor.org/stable/1808111
• Bernanke, Ben S., Cara S. Lown, and Benjamin M. Friedman. 1991. “The Credit Crunch.”  Brookings Papers on Economic Activity , 1991(2): 205.  https://www.doi.org/10.2307/2534592
• Board of Governors of the Federal Reserve System. 2013. “Dodd-Frank Act Stress Test 2013: Supervisory Stress Test Methodology and Results.” Technical Report, Board of Governors of the Federal Reserve System.  https://www.federalreserve.gov/newsevents/press/bcreg/dfast_2013_results_20130314.pdf
• Board of Governors of the Federal Reserve System. 2019. “Federal Reserve Board Releases Scenarios for 2019 Comprehensive Capital Analysis and Review (CCAR) and Dodd-Frank Act Stress Test Exercises.” Press Release.  https://www.federalreserve.gov/newsevents/pressreleases/bcreg20190205b.htm
• Calomiris, Charles W., and Joseph R. Mason. 2003. “Fundamentals, Panics, and Bank Distress during the Depression.”  American Economic Review , 93(5): 1615–1647.  http://www.jstor.org/stable/3132145
• Calomiris, Charles W., and Berry Wilson. 2004. “Bank Capital and Portfolio Management: The 1930s ‘Capital Crunch’ and the Scramble to Shed Risk.”  The Journal of Business , 77(3): 421–455.  https://www.doi.org/10.1086/386525
• Campello, Murillo. 2002. “Internal Capital Markets in Financial Conglomerates: Evidence from Small Bank Responses to Monetary Policy.”  The Journal of Finance , 57(6): 2773–2805.  https://doi.org/10.1111/1540-6261.00512
• Cetorelli, Nicola, and Linda S. Goldberg. 2012. “Banking Globalization and Monetary Transmission.”  The Journal of Finance , 67(5): 1811–1843.  https://doi.org/10.1111/j.1540-6261.2012.01773.x
• Cortés, Kristle R., Yuliya Demyanyk, Lei Li, Elena Loutskina, and Philip Strahan. Forthcoming. “Stress Tests and Small Business Lending.”  Journal of Financial Economics .  https://doi.org/10.1016/j.jfineco.2019.08.008
• Cortés, Kristle R., and Philip E. Strahan (2017). “Tracing Out Capital Flows: How Financially Integrated Banks Respond to Natural Disasters.”  Journal of Financial Economics , 125(1): 182–199.  https://doi.org/10.1016/j.jfineco.2017.04.011
• Covas, Francisco. 2017a. “The Capital Allocation Inherent in the Federal Reserve’s Capital Stress Test.” Technical Report, The Clearing House.  https://www.theclearinghouse.org/research/articles/2017/01/-/media/20d957fe6fdc4607b5c24eb8506a5de5.ashx
• Covas, Francisco. 2017b. “Capital Requirements in Supervisory Stress Tests and Their Adverse Impact on Small Business Lending.” The Clearing House, Staff Working Paper 2017-2.  https://www.theclearinghouse.org/research/articles/2017/08/capital-requirements-supervisory-stress-tests-adverse-impact-small-business-lending
• Houston, Joel, Christopher James, and David Marcus. 1997. “Capital Market Frictions and the Role of Internal Capital Markets in Banking.”  Journal of Financial Economics , 46(2): 135–164.  https://doi.org/10.1016/s0304-405x(97)81511-5
• Kashyap, Anil K., and Jeremy C. Stein. 1995. “The Impact of Monetary Policy on Bank Balance Sheets.”  Carnegie-Rochester Conference Series on Public Policy , 42: 151–195.  https://doi.org/10.1016/0167-2231(95)00032-u
• Kashyap, Anil K., and Jeremy C. Stein. 2000. “What Do a Million Observations on Banks Say about the Transmission of Monetary Policy?”  American Economic Review , 90(3): 407–428.  https://doi.org/10.1257/aer.90.3.407
• Peek, Joe, and Eric S. Rosengren. 1997. “The International Transmission of Financial Shocks: The Case of Japan.”  American Economic Review , 87(4): 495–505.  https://www.jstor.org/stable/2951360
• Peek, Joe, and Eric S. Rosengren. 2000. “Collateral Damage: Effects of the Japanese Bank Crisis on Real Activity in the United States.”  American Economic Review , 90(1): 30–45.  https://doi.org/10.1257/aer.90.1.30

Suggested Citation

Demyanyk, Yuliya. 2019. “Have Stress Tests Impacted Small-Business Lending?” Federal Reserve Bank of Cleveland,  Economic Commentary  2019-19. https://doi.org/10.26509/frbc-ec-201919

Economic Effects of Tighter Lending by Banks

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FRBSF Economic Letter 2024-11 | May 6, 2024

Banks tightened the criteria used to approve loans over the past year. Analysis shows that their tighter lending standards can be partially explained by economic conditions that reduce demand for loans and increase their potential risk, such as policy rate increases and a slowing economy. The unexplained part may reflect a restrained credit supply, specifically related to banks being less willing or able to take on risk. What are the potential economic consequences? Past credit supply shocks have had significant long-lasting effects on unemployment but less impact on inflation.

The first half of 2023 was characterized by credit market turbulence, including the collapse of Silicon Valley Bank, Signature Bank, and others. The increased uncertainty in the banking sector that followed these closures led many banks to tighten their credit standards, becoming stricter about the conditions under which they were willing to lend. According to the Senior Loan Officer Opinion Survey on Bank Lending Practices (SLOOS), lending standards in 2023 tightened to a degree only seen during the Global Financial Crisis and the COVID-19 pandemic. This leads to questions about the possible effects on the overall economy, particularly whether tighter standards resulted from an unexpected drop in the credit supply or other economic factors, such as higher interest rates or a slowing of the economy.

In this Economic Letter , I analyze supply and demand factors in credit market conditions and their impact on bank lending standards, like Lown and Morgan (2006) and others. A measure based on reports from bank loan officers shows that credit conditions began tightening in mid-2022, well before the bank closures. Since early 2023, about half of the tightening in lending standards has been due to changes in the credit supply specific to the banking sector, such as banks reevaluating their willingness to take on risk, and the remainder in response to other economic conditions. My analysis also estimates that unexpected changes to credit supply conditions—including the March 2023 bank closures—can account for 0.4 percentage point of unemployment by the end of 2023, meaning that unemployment in that quarter would have been 3.3% without the credit supply shock. My estimates suggest that the effects related to these credit supply shocks will be persistent, lasting through 2026. The contribution of these shocks to inflation is likely to be more subdued, pushing core personal consumption expenditures (PCE) inflation down by less than a 0.1 percentage point through 2026.

Bank lending standards and the economy

Bank lending to businesses depends on two key components: the loan interest rate and the lending standards that businesses need to meet to qualify for a loan. When banks are more willing to take on risk, they impose minimal lending standards; by contrast, when banks prefer to take on less risk, they scrutinize borrowers more and impose stricter conditions. The interest rate on loans responds to both credit supply and credit demand conditions. By contrast, lending standards are more directly related to the willingness or ability of banks to tolerate risk. Thus, they can be used as a proxy for credit supply conditions.

Using SLOOS data, I measure commercial and industrial bank lending conditions as the percentage of responding banks that report tighter lending standards minus the percentage that report easing of lending standards. The resulting measure can range from –100, meaning that all banks are easing standards, to 100, meaning that all banks are tightening standards. A positive (negative) number means that it is harder (easier) for firms to get credit. This method has been used by Lown and Morgan (2006) and other studies to measure credit supply conditions. Figure 1 shows the evolution of this measure from 2007 through 2023 for lending to medium and large businesses (blue line) and to small businesses (green line).

Figure 1 Tightening in commercial and industrial lending standards

The figure shows that lending standards tightened in 2023 to a degree seen only during the Global Financial Crisis in 2008 and the onset of the COVID-19 pandemic in 2020. It also clearly shows that lending standards began tightening in the second half of 2022, well before the bank collapses in early 2023. Finally, the tightening in 2023 lending standards was very similar for all businesses regardless of their size. Therefore, I use the measure for medium and large firms as representative for the economy.

I use this measure in statistical analysis to estimate how credit conditions interact with the rest of the economy, particularly to understand their impact on unemployment and inflation. To measure unemployment, I focus on the unemployment gap, calculated as the difference between the measured unemployment rate and the Congressional Budget Office measure of the potential unemployment rate, and I use the core personal consumption expenditures (PCE) price index to measure inflation.

My approach builds on the work of Lown and Morgan (2006), combining this measure of credit conditions with other measures of financial conditions. These include the effective federal funds rate, the 10-year Treasury constant maturity yield, the 30-year fixed mortgage rate spread relative to the 10-year Treasury yield, the BAA corporate bonds yield spread relative to 10-year Treasury bonds, and bank loans. Finally, to reflect forces that have recently been important in shaping the economy and inflation—namely supply chain pressures and significant changes in energy prices—I also include the West Texas Intermediate spot oil price, and the Federal Reserve Bank of New York’s Global Supply Chain Pressures Index. The analysis uses data from 1998 through the second quarter of 2023.

My statistical model considers interactions between the different variables within the same quarter and over time. The SLOOS lending standards measure is observed early in the quarter and corresponds to bank responses from the previous quarter. Shocks to this measure are thus identified as changes in lending standards that do not respond to other variables within the same quarter. That is, tightening of standards can respond to this identified shock in the same quarter, and to other types of shocks from previous quarters.

Figure 2 shows how much a 10 percentage point tightening in lending standards affects the unemployment gap and inflation. The horizontal axis shows the number of quarters since the shock took place. The vertical axis shows the increase in percentage points for each variable relative to the absence of tighter lending standards, with zero meaning no change in outcomes. The solid blue line is the median estimate, and the shaded areas represent the 70% (darker) and 90% (lighter) probability ranges of possible estimates.

Figure 2 Response of unemployment and inflation to a 10 percentage point tightening of lending standards

Note: Shading represents 70% (darker) and 90% (lighter) probability estimates around median estimate. Source: Senior Loan Officer Opinion Survey on Bank Lending Practices and author’s calculations.

Overall, the tightening in lending standards induces a persistent increase in the unemployment gap and a small drop in inflation. The impact on unemployment is expected: tighter lending standards imply that firms cannot invest as much, reducing demand for credit in the economy. With weaker demand, firms will hire fewer workers and lay off some of their workforce, leading to higher unemployment.

The impact on inflation is more nuanced. On the one hand, a weaker demand for credit eases price inflation. On the other hand, as documented in Gilchrist and Zakrajsek (2012), tighter lending standards are also associated with higher interest rates, which increase operational costs for firms. Firms will pass some of those higher costs to their customers, leading to price inflation. Model estimates suggest the demand effect is more likely to prevail, and inflation falls slightly on net in response to tighter lending standards.

What led to tight lending standards in 2023?

Was the 2023 tightening in lending standards a pure credit supply shock, or was it a natural response of banks to evolving economic conditions? To address this question, I compare actual data at each point in time with the model’s predictions for that time to extract each component’s response to past shocks. I use the results to determine how much of the actual response of each component is due to shocks of different sources—for example, how much of the changing lending standards comes from responses to supply chain shocks or credit supply shocks.

The analysis suggests that credit supply shocks account for about 23 percentage points of the tighter lending standards in the first half of 2023. The remaining 22 percentage points of the tightening is associated with the response of lending standards to changes in economic conditions due to supply chain pressures and other factors originating outside the credit market.

The measure also shows that lending standards started to tighten before 2023, as early as the second quarter of 2022, as shown in Figure 1. This suggests that inflationary pressures and monetary policy tightening in previous quarters played a role in banking conditions more generally. Therefore, tighter credit standards may be related to the bank collapses in that they shared similar root causes in recent economic and financial conditions. However, credit supply factors in the first half of 2023 that could be associated with bank closures explain only part of the overall tightening of lending standards. Furthermore, my model estimates that the impact of the credit supply shock on tighter lending standards will be relatively short lived, dissipating by the end of 2024.

I next use this methodology to estimate how much credit supply shocks contributed to unemployment and inflation in the recent past and how much they are expected to contribute through 2026. To do this, I combine the estimated size of the shocks with the estimated responses of the economy to those shocks. The bars in Figure 3 show the median estimated contribution to unemployment.

Figure 3 Contribution of credit supply shocks to unemployment

The estimated contribution for the last quarter of 2023 is 0.4 percentage point, which means that unemployment would have been 3.3% without the credit supply shock, rather than the 3.7% reported in the data. My analysis shows that, even though the tightening of lending standards is not expected to last long, the effects on unemployment are estimated to persist through 2026. For inflation, the contribution of the credit supply shock is more subdued but more persistent, pulling inflation down by less than 0.1 percentage point through the entire projection into 2026. A persistent increase in corporate bond yield spreads implied by the credit supply shock may explain why the effects on the rest of the economy last so long.

This analysis has several limitations, including the possibility that underlying economic relations changed with the COVID-19 pandemic. Related to this, the model is proportional, implying that a shock of twice the size would have effects that are also twice the reported size. However, the unusually large shocks in this analysis could trigger more than proportional economic responses—for example, if cascading bank failures induced snowball effects in the economy due to an increasingly fragile banking system. Finally, using different measures to proxy for financial and monetary policy conditions could result in different estimates, although my tests using different data yielded similar results to those reported here.

In the first half of 2023, lending standards tightened substantially. This Letter finds that only about half of the tightening resulted from a credit supply shock that would have caused a slowdown in economic activity, while the remainder corresponds to banks’ normal response to overall economic conditions. While a tightening of lending standards is not expected to persist for very long, this analysis suggests it could add half a percentage point to unemployment through 2024 and push down inflation by a small amount.

Lown, Cara, and Donald P. Morgan. 2006. “The Credit Cycle and the Business Cycle: New Findings Using the Loan Officer Opinion Survey.” Journal of Money Credit and Banking 38(6).

Simon Gilchrist and Egon Zakrajsek. 2012. “Credit Spreads and Business Cycle Fluctuations.” American Economic Review 102(4, June), pp. 1,692–1,720.

Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System. This publication is edited by Anita Todd and Karen Barnes. Permission to reprint portions of articles or whole articles must be obtained in writing. Please send editorial comments and requests for reprint permission to [email protected]

7 Government Small Business Grants to Apply For in May 2024

Seeking funding is a right of passage for many small business owners. While there are endless private and government-backed loans to choose from, if you’re looking to evade strict repayment terms and steep interest rates, it could be worth considering government business grants.

Government business grants are financial awards issued by federal, state, or local authorities. There are thousands of grants up for grabs through government website portals, but since this type of financing is designed to support the public, their eligibility criteria tend to be quite specific.

If you’re interested in pursuing this type of finance, we round up some government grants small businesses can apply for in May, including their specialisms, funding limits, and deadlines. We also offer some advice for writing your application, to make sure your proposal is as competitive as possible.

In this guide: 

Government Small Business Grants to Apply For in May 2024

Tips for perfecting your government grant application.

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There are thousands of government funds to apply for. If you want to cut through the noise, take a look at some of the most popular options below:

• Small Business Innovation Research (SBIR) program
• Small Business Technology Transfer (STTR) program 
• Women-Owned Small Business (WOSB) Federal Contracting program
• 8(a) Business Development Program 
• HUBZone Program
• Small State Business Credit Initiative (SSBCI)
• U.S. Department of Commerce Minority Business Development Agency (MBDA)

1. Small Business Innovation Research (SBIR) program

• For: Small businesses interested in carrying out innovation research
• Funding limit: Over $2 million
• Deadline: September 5, January 5, and April 5

The Small Business Innovation Research program was designed by the Small Business Administration to encourage US businesses to engage in Federal research and development. The competitive program is open to select small businesses and specifically encourages participation from women and socially or economically disadvantaged persons.

To be eligible for the SBIR program, your business must be for profit, be over 50% owned by permanent residents of the US, and have fewer than 500 employees. To apply for the grant, you need to register your business with SBIR, if you haven’t already, submit a proposal before one of the program’s tri-annual deadlines, and then respond to feedback and refine your concept if necessary.

Learn more about the SBIR grant, and how to apply here .

2. Small Business Technology Transfer  (STTR) programs

• For: Small businesses that have paired up with a research institution

Like the SBIR, the Small Business Technology Transfer program is a government program focused on developing innovative solutions to pressing problems across the US. This type of funding aims to facilitate cooperative research and development efforts research between small business concerns and non-profit US research institutions, with the potential for commercialization of innovative technological solutions.

However, unlike the SBIR, this program requires the small business applicant to be teamed up with a non-profit research institution already, which typically takes the form of a university or Federal Laboratory. The STTR program is also focused on the transfer of technology from the research institution, rather than just the research alone.

Aside from being paired with a research institution, STTR’s eligibility criteria are nearly identical to SBIR’s.

Learn more about the STTR grant, and how to apply here .

3. Women-Owned Small Business (WOSB) Federal Contracting Program

• For: Women-owned businesses
• Funding limit: $4 million for service contracts and $6.5 million for manufacturing contracts
• Deadline: Rolling

The Women-Owned Small Business Federal Contracting Program was designed to build a level playing field for female business owners. The contracts are designated for specific industries where female-owned businesses are underrepresented. You can see which industries are eligible for the grant program here .

To be eligible for this program, you need to run a small business, have the business be at least 51% owned and controlled by US women, and have an economically disadvantaged woman manage the day-to-day operations and make long-term decisions.

Learn more about WOSB, and how to apply here.

4. 8(a) Business Development Program

• For: Socially and economically disadvantaged business owners
• Funding limit: $7 million for acquisitions assigned manufacturing NAICS codes and $4.5 million for all other acquisitions

The 8(a) program is a nine-year program created by the SBA to financially support firms owned and controlled by socially and economically disadvantaged individuals. It’s designed to span nine years and helps eligible businesses access new business paths from government contracting.

Since the creation of the program in 1970, it has helped disadvantaged businesses gain access to billions of dollars in funding. To be eligible for the government grant, you must run a small business, be at least 51% owned and controlled by US citizens who are socially and economically disadvantaged, have a personal net worth of under $805 thousand, and demonstrate good character.

Learn more about the 8(a) business development program, and how to apply here .

5.  HUBZone Program

• For: Small businesses in historically under-utilized business zones
• Funding limit: $3.5 million for products and services, and $5.5 million per contract for manufacturing

The HUBZone program is a SBA initiative designed to promote economic development and job growth in historically underutilized business zones (HUBZones). The program does so by offering financial grants to business owners operating within these communities.

To be eligible for this business grant you need to run a small business, have the business be at least 51% owned and controlled by a Community Development Corporation, an agricultural cooperative, an Alaska Native corporation, a Native Hawaiian organization, or an Indian tribe, have its main office located in a HUBZone, and have at least 35% of it employees living in the HUBZone for at least 45 days before applying.

Learn more about the HUBZone program, and how to apply here .

6. Small State Business Credit Initiative (SSBCI)

• For: Small businesses run by socially and economically disadvantaged individuals
• Funding limit: $20 million

The Small State Business Credit Initiative is a federal program designed to support entrepreneurship across the US. The grant program is provided by the US Department of the Treasury and was expanded by President Biden’s American Rescue Plan Act in 2021, providing an extra $10 billion in funding to eligible businesses.

In addition to providing capital support to small businesses, SSBCI can also provide technical assistance to eligible businesses through its Technical Assistance (TA) Grant Program. The SSBCI is available to businesses owner-occupied small businesses with 500 employees or less, and is specifically tailored to small businesses owned and controlled by socially and economically disadvantaged (SEDI) owners and very small businesses with less than 10 employees.

Learn more about the SSBCI program, and apply here .

7. U.S. Department of Commerce Minority Business Development Agency (MBDA)

• For: Small businesses run by minorities
• Funding limit: Up to $350,000 for the first 10 months

The U.S. Department of Commerce Minority Business Development Agency (MBDA) is a Federal grants program designed to promote the growth of minority-owned businesses. The ultimate aim of the program is to provide minority business enterprises (MBEs) with access to funds, contracts, and market opportunities both in the US and globally.

To be eligible for MBDA assistance, a business must be owned or controlled by one or more socially or economically disadvantaged persons. The majority of business owners must also identify as racial minorities.

To apply for an MBDA business grant, you need to register your business with SAM.gov and Grants.gov if you haven’t already, align your proposal with the stated requirements, and submit your application before the deadline.

Learn more about the grant, and how to apply here .

Government grants offer a golden opportunity to businesses looking to grow or recover their business. However, due to the competitive nature of the financing, you need to ensure your grant proposal is polished and stands out from the crowd.

We understand that writing a grant application might seem like a daunting process, especially if you’re a first-timer. So, to give your proposal the best chance possible of succeeding, take heed of these pointers below.

• Give yourself enough time –  You don’t want to be writing a grant application against the clock. Writing a proposal can take much longer than you expect, so to account for unexpected hold-ups we recommend giving yourself at least 45 days to complete your written application.
• Follow the instructions carefully – Don’t go off-piste when writing your application. Make sure you include all the information requested by the agency, and present it in the correct format.
• Be as concise and clear as possible – Ensure your application is written in clear, simple language, and use as many candid examples as possible to paint a clear image for your reader. If you use any graphs or imagery, make sure you label them clearly as well.
• Keep the audience in mind – The likelihood is that the reviewer won’t already be familiar with your business. To make sure you won’t gloss over necessary information write the proposal for an audience that’s hearing about your business for the first time.
• Develop a proofreading strategy – You don’t want to hamper your application’s success with silly mistakes like typos or grammatical errors. So, to ensure your proposal looks polished carefully proofread the application or outsource the service to a professional.

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