Congress enacted The Home Mortgage Disclosure Act (HMDA) in 1975 to combat credit shortages in urban neighborhoods throughout the United States. The government believed that financial institutions contributed to the decline in cities across the Rust Belt, from Detroit to St. Louis, due to discriminatory lending practices.

The Federal Financial Institution’s Examination Council (FFIEC) collects and discloses data about applicant and borrower characteristics to help identify possible discriminatory lending patterns and enforce anti-discrimination statutes.

Its primary purpose is to provide data, but it merely supplies the data — no government agency interprets the data. In other words, it’s up to a discerning public to scrutinize the data and determine whether discriminatory malfeasance has occurred.

After an initial scan of the database, we saw a disturbing trend: 26% of African-American applicants were denied mortgages compared to 10% of white-American applicants. Before crying wolf, we needed to dig further into this data, using what was available to control for potential extraneous variables.

After collecting and analyzing over 1.7 million applicants from 2016 (read more about our methodology here), two things became clear:

1. Even when controlling for income, African Americans are twice as likely to be denied a mortgage than white applicants
2. The applicant data points are limiting, and HMDA’s data set is missing important variables like why applicants were denied

In this study, we’ll dive deep into our key findings, showing you which regions of the country African Americans are most likely to experience lender discrimination, causes and effects of lender discrimination, and what needs to change for lenders to be held accountable.

Key Findings

• Racial discrimination still exists in mortgage lending: African Americans are twice as likely to be denied a mortgage when controlling for income
• Disparity between white and black mortgage approval rates is most pronounced in the South: 89% of white applicants are approved in Southern states, compared to 76% of black applicants when controlling for income
• The West has the least racial disparity between white and black applicants, but the difference between approval rates is still statistically significant, indicating racial discrimination in the mortgage industry is a nationwide issue
• Mortgage applicants are overwhelmingly white: Of our 1.7 million applicants sampled, 1,482,248 mortgage applicants were white, compared to 80,442 African Americans, 93,762 Asian Americans, 29,293 American Indians, and 15,645 Native Hawaiian or Pacific Islanders
• The Home Mortgage Disclosure Act (HMDA) grew out of public concern over credit shortages in urban neighborhoods, but the data is alarmingly sparse: 52% of black applicants had no reason listed for their mortgage being denied — i.e., the data set is incomplete in important areas needed for careful scrutiny
• African-American and Hispanic home buyers are respectively 105% and 78% more likely to use high-cost mortgages for home purchases, putting them at greater risk of foreclosure

Insights & Analysis

African-American mortgage applicants are twice as likely to be denied credit as white applicants

In 2016, 19% of African Americans were denied mortgages compared to 9% of white applicants, when controlling for applicant incomes.

Racial discrimination in lending was most pronounced in Southern states. 24% of African Americans were denied mortgage applications in the South, compared to 11% of white applicants when controlling for income. The West has the least racial disparity between white and black applicants, but the difference between approval rates is still statistically significant, indicating racial discrimination in the mortgage industry is an issue nationwide.

The states where black applicants are least likely to get approved include Kansas, South Carolina, Mississippi, Louisiana, Arkansas, Delaware, and Alabama. In states like South Carolina, 49% of black applicants were denied applications compared to 8% of white applicants, not controlling for income.

For further context, there are only four states where white applicants are denied at a higher rate than African Americans: Montana, Idaho, Hawaii, and Vermont; and in these states the discrepancy between black and white approval rates is less than 7%. However, in states like South Carolina, there’s a 42% discrepancy between black and white approval rates.

Not only are African Americans denied mortgages at a higher rate, but they’re less likely to apply for mortgages in the first place. The FFIEC reported 3,673,959 white Americans applied for mortgages in 2016, compared to only 342,387 African Americans. About 0.85% of the African American population applied for mortgages compared to 1.52% of the white population in the U.S.

52% of black applicants had no reason for being denied a mortgage

A study from Reveal and The Center for Investigative Reporting found that racial disparities exist in 61 metro areas across the country, including Atlanta, Detroit, Philadelphia, St. Louis, and San Antonio.

Reveal conducted interviews with lenders and home buyers to get a picture of why these racial discrepancies exist. While mortgage lenders don’t dispute they deny loan applications from people of color at higher rates than white people, they attribute the disparity to hidden factors like credit scores.

New Jersey-based TD Bank, which denied a higher proportion of black and Latino applicants than any other major lender, said it "makes credit decisions based on each customer’s credit profile, not on factors such as race or ethnicity."

Here’s the problem: credit scores, debt-to-income ratio (DTI), and other important control variables aren’t included in the HMDA’s database. These key applicant metrics are not mandated by the HMDA, so lenders aren’t required to disclose this critical information to the public.

Another critical piece of information, why the loan was denied, is an optional field for mortgage lenders not regulated by the Office of the Comptroller of the Currency.

What are the results of these lax reporting standards?

52% of African American applicants have no exact reason their application was denied, the highest of any race. So while we know the most common reasons African Americans were denied mortgages were credit history and debt-to-income ratio, we do not understand why over half of the applicants were specifically denied. The Dodd-Frank Act explicitly amends HMDA to require lenders to disclose important data points about applicants — like credit score and debt-to-income ratio — to the public while keeping applicant identities confidential.

Unfortunately, the American Bankers Association (ABA) wants to keep credit scores and other important variables out of lender disclosures. According to a 2017 April policy paper, the ABA stated amending HMDA to require more data collection would be expensive and adds volumes of irrelevant data. The "volumes of irrelevant data" the ABA refers to are exactly the same metrics lenders use to assess applicants' creditworthiness, like credit scores. These amendments would also reveal whether applicants are offered above-average APRs and unconventional lender fees.

While this data would be valuable in assessing lender discrimination, there’s also the question of whether credit scores have an inherent bias built into their scoring algorithms against people of color. Credit scores are a black box with hundreds of variables, and studies have shown that credit score algorithms have a "disparate impact on people and communities of color." According to these studies, the current credit-scoring systems penalize borrowers who have anything other than mainstream loans, and people of color are funneled towards high-cost, high-risk loans.

African American and Hispanic applicants are funneled towards high-cost loans with greater foreclosure rates

A study from the National Bureau of Economic Research shows that African-American and Hispanic mortgage applicants are respectively 105% and 78% more likely to use high-cost mortgages for home purchases. High-cost home loans come with higher fees and interest rates, and, as a result, applicants are more likely to default on their mortgages.

This all makes sense: African Americans are denied conventional mortgages at a higher rate than whites, resulting in more applications for high-cost, higher-risk mortgages.

The study found that "differential exposure" to high-risk lenders combined with "differential treatment" by lenders explains most of the racial and ethnic differences in high-cost mortgage lending. In other words, African Americans and Hispanic borrowers are treated differently by borrowers and funneled to high-cost mortgages.

African-American borrowers were more likely to receive subprime loans at higher costs, possibly contributing to higher foreclosure rates among these borrowers.

The Dodd-Frank bill could shine light through muddy lending practices

If there’s one sliver of hope within this rather bleak analysis, it’s that the Dodd-Frank bill requires several new applicant metrics to be recorded by mortgage lenders for 2018 data sets and beyond. Data points like credit scores, fees, prepayment penalties, and interest rates will be available to the public, allowing for greater transparency into predatory and discriminatory lending practices.

We know discriminatory lending is happening, and this data will allow concerned citizens to identify the who, where, and how. Unfortunately, it’s taken 44 years of overly lenient and inadequate standards created by the HDMA to move towards a more transparent future.

Note: We’ll be conducting a followup study once the 2018 data is released.

Methodology

We used the Home Mortgage Disclosure Act (HMDA) database on home purchases for all 50 states and Washington, D.C., and all lenders in 2016 to build the dataset for our analysis. We limited the dataset to approved and denied applications by the financial institution, and applications by Asian, African-American, and white applicants and co-applicants, who self-identified their race on their mortgage applications.

The final dataset included information containing approximately 1.7 million applicants regarding their race, income, approval decision, and denial reasons.

We divided states into four regions (Northeast, Midwest, South and West) to examine regional differences in approval rates. Before modeling, the income measure was log transformed and centered. We ran generalized linear models to investigate whether applicants’ race and region influenced mortgage approval rates on home purchases while controlling for applicants’ income.

We did not include Native American and Pacific Islanders in this analysis because there was not enough mortgage application data to run a statistically significant test.

For specific questions about our analysis email thomas@movewithclever.com and CC eylul@movewithclever.com.

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Whether it's a house with a white picket fence in the suburbs or a high-rise apartment in the heart of a vibrant city, owning property is considered part of the "American Dream" by the vast majority of Americans. However, for many Americans, that dream has become a nightmare because of ever-increasing home prices and stagnant wages.

To understand how expensive the American Dream has become — and whether it is still achievable today — we gathered Census data from 1960 to 2017 on home prices, rents, and household income. After adjusting for inflation over time, the future of the American Dream seems rather gloomy: Median home prices increased 121% nationwide since 1960, but median household income only increased 29%.

Home buyers aren’t the only ones struggling. Median gross rent increased by 72% since the 1960s, more than twice the growth seen by adjusted incomes, making renting costlier than ever and saving for a future home difficult.

Rising rents and increasing home prices make it harder than ever to save for a down payment and afford monthly mortgage payments. In this report, we'll dig deeper into which areas of the country the average homeowner can afford, and which housing markets are headed for regression because of an imbalance of home prices relative to household incomes.

Here are the nationwide insights you need to know.

Key Insights

• Median home prices have increased at four times the rate of household incomes since 1960, leading to imbalanced price-to-income ratios in most major metropolitan areas.
• Nationwide rents have increased at twice the rate of household incomes since 1960, making saving for a down payment increasingly difficult.
• A healthy price-to-income ratio is 2.6 (i.e. it would take 2.6 years of median household income to purchase the median home), but the nationwide price-to-income ratio hasn't been healthy since the late 1990s.
• Only 16 out of the 100 most populated cities in the United States are below a 2.6 price-to-income ratio in 2019.

A historical view of home price-to-income ratios

Let’s start with an important metric to help us understand and contextualize how expensive buying a home has become: price-to-income ratio.

Price-to-income ratio is the median home price divided by the median household income in an area (Note: some researchers use disposable income, but for our purposes we'll be using median household income).

This is a gauge of how long it will take home buyers to save for a down payment, and whether they’ll be able to afford their monthly mortgage payments.

According to City Lab, the rule used by top real estate agents is that you can afford a home equal to roughly 2.6 years of your household income, i.e., a 2.6 price-to-income ratio. We can also use price-to-income ratio to assess how healthy a housing market is — can the median resident save for a down payment within a reasonable time frame? In the 1960s, the price-to-income ratio was 2, meaning that two years of household income was enough to purchase a house.

Since the 1960s, however, the difference between home prices and income has nearly doubled. By 2017, the nationwide price-to-income ratio was 3.6, showing over 3.5 years of household income was necessary to purchase a house. In fact, the nationwide price-to-income ratio hasn't been at a healthy balance since the 1990s. Homes are increasingly unaffordable, leading to unstable housing markets where demand can't meet supply.

Home prices in the West are increasingly out of line with household income, while only 16 out of the 100 most populated areas in the U.S. are below the healthy 2.6 price-to-income ratio.

While these numbers are concerning, taking a regional approach can help us understand what's driving these large discrepancies between household income and home prices to determine where homeownership is realistic for the average American.

Let's break down each region of the U.S. and analyze specific cities.

Important Takeaways by Region

• Real estate in the West is becoming unobtainable: Median home prices increased by 195% since the 1960s, while median household income only increased by 26%.
• The Midwest might be the last region homeowners can realistically afford. There’s almost no gap between rental and household income growth rates, so Midwesterners can save for their down payment and afford the median mortgage payments in their cities.
• The gap between household income and home prices was pronounced in the Northeast, but following the 2008 crash, the gap has narrowed as household incomes rise and home prices have dropped throughout the region.
• Home prices in the South were consistent with household income increases until the 2000s when the market became unstable. Home values increased 75% from 2000 to 2017 and continue to climb.

The West: Where the American Dream goes to die

The West includes the following states: Alaska, Arizona, California, Colorado, Hawaii, Montana, New Mexico, Oregon, Utah, Washington, and Wyoming.

In 1960, the price-to-income ratio for Western states was 2.1, but by 2017 it increased to 4.9. While median home prices increased by 195% in the West, median household income only increased by 26% since the 1960s. This means the growth rate of home prices is 7.5 times more than the growth rate of household income, making the Western region the least affordable region in the U.S. The average real estate commission fee in this areas ends up being substantially higher than any other regional housing market.

The huge difference in growth rate appears to be driven by coastal metropolitan areas and new hubs such as Denver. To investigate this possibility, we looked at San Francisco, CA, Los Angeles, CA, Seattle, WA, and Denver, CO.

In the 1960s, the price-to-income ratios for all these metros were below 2.6. From the 1980s on, the ratio for San Francisco and Los Angeles climbed to 4.7 and 5.6 respectively, reaching its peak before the 2008 financial crisis at 9.2 and 8.8. These latter values mean that in 2008, nine years of median household income was necessary to purchase a house in these metros. Interestingly, the financial crisis barely impacted price-to-income ratios. In 2017, price-to-income ratios were still very similar to those during the housing bubble: 8.8 for San Francisco, and 8.4 for Los Angeles.

Although price-to-income ratios were not as high for the two new tech hubs, Seattle and Denver, they either doubled or almost doubled the healthy average of 2.6. In 2017, the price-to-income ratio was 5.4 for Seattle and 5.1 for Denver.

The growth rates draw a similarly discouraging picture for homeownership in these metros. Since 1960 median household income grew by 59% and 56% in Seattle and Denver, respectively, whereas median home prices grew by 286% and 239%.

In Los Angeles, the growth rate of median household income since 1960 has been slower than the national average, yet the growth rate of median home prices was faster with 358%, showing that while income increase over the years was not comparable to other larger metros, home prices more than quadrupled.

Historical housing data for San Francisco paints a similar picture. San Francisco is one of the few metro areas that almost doubled its median household income since 1960 with a growth rate of 91%. San Francisco's tech boom has led to an increase in high demand, high-paying jobs. However, home prices increased 531% since 1960, reserving homeownership for the hyper-rich, despite the financial growth of the metro. For context, the median home price in 1960 adjusted for 2017 inflation was $134,713, whereas in 2017, the median home price was $849,500. That means you'd need to save $169,900 for a 20% down payment.

It appears the American Dream of homeownership is dead in many parts of the West.

The Northeast: Home prices are slowly coming back down to earth

The Northeast includes Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont.

In the 1960s, owning a house was affordable in the Northeast, with a price-to-income of 2.1. However, home values started to outscale household income in the 1980s, with a price-to-income ratio of 3.7 by 1990. The price-to-income ratio reached its peak around the 2008 financial crisis with 4.6 and dropped to 4.0 in 2017.

However, the growth rate of home prices is 4.2 times more than the growth rate of household income, making the Northeast the second least affordable region.

However, a surprising trend emerged in our data between 2000 and 2017. We observed an increase of 110% in home prices between 2000 and 2008 (i.e., before the financial crisis), and a decrease of 24% between 2008 and 2010 (i.e., after the financial crisis), which real estate analysts expected. However, unusually, home prices dropped by a further 18% between 2010 and 2017, while household income increased by 9% between these years.

This trend is in contrast with the trend observed between 2010 and 2017 nationwide and in the Western region. If this trend continues, there may be still hope for home buyers in the Northeast.

To examine whether this trend applies to coastal metros and inland ones and whether there are affordability differences between these areas, we looked at New York City, NY, Boston, MA, Albany, NY, and Pittsburgh, PA.

The data from New York City confirmed the overall trend observed in the Northeast. By 2017, the price-to-income ratio was 5.8 in New York City and median home prices increased by 184% since the 1960s, compared to a 54% increase in median household income. Although these statistics reveal that New York City is a cost-burdened metro area to purchase a house, the housing values between 2010 and 2017 suggests a potentially more optimistic housing market for the future. Home prices continued to decrease by 24% between 2010 and 2017, whereas household income increased by 12%, reducing the growth rate gap between home prices and household income.

However, this decrease over the last few years does not seem to be the trend in all coastal metros. In Boston, home prices increased by 24% between 2010 and 2017, after dropping by 25% during the financial crisis, and median home prices increased 228% since 1960.

The inland metros, Albany and Pittsburgh, are more affordable compared to their coastal counterparts. The price-to-income ratios of these metros were 3.2 and 2.6 in 2017, respectively. In Albany, median home prices increased by 132% since 1960.

Median household income increased by 50% since 1960, which is higher than the national average.

Similar to the trends we observed in the Northeast and New York City, between the years of 2010 and 2017, home prices decreased by 7%, whereas household income increased by 14%, reducing the growth rate gap between these two measures.

In Pittsburgh, homeownership seems even more attainable, with median home prices showing an increase of 64% and median household income showing an increase of 31% over the years. Thus, the growth rate difference between home prices and income was much lower compared to coastal metros. Although home prices showed an increase of 18% between 2010 and 2017, an increase of 14% closely followed this increase for household income.

The future of homeownership is complicated in the Northeast. Overall, the inland metros represent more affordable conditions and even for the major coastal metros like New York City, there might be hope.

The South: Still affordable, but for how long?

The South includes Alabama, Arkansas, Delaware, D.C., Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia.

Compared to the Western and Northeastern regions, the South does not show as much of a discrepancy between home prices and household income.

From 1960 to 2000, price-to-income ratios were around 2.6, making homeownership attainable during these years. Home prices jumped during the 2000s and kept steam through the housing crisis.

However, a worrying trend that emerged over time for the South is that the discrepancy between the growth rates of home prices and household income has been climbing since the 2000s. Although the growth rate of income was almost stable between 2000 and 2017 (an increase of 2%), the growth rate of home prices almost doubled (an increase of 75%), hinting that the South might not remain as affordable, if a similar trend continues.

To investigate whether the observed discrepancy increase between growth rates holds true for some metropolitan areas, we looked at Charlotte, NC, Columbia, SC, and Oklahoma City, OK.

In 2000, the growth rate differences between home prices and household income were 17%, 13% and 23% for Charlotte, Columbia and Oklahoma City, respectively. By 2017, however, the same growth rate differences increased to 66%, 56%, and 82%, but household income couldn't keep up, causing these metro areas to be less affordable relative to prior years.

The good news is even with this increasing discrepancy between the growth rates over the last years, in 2017 the price-to-income ratios were 3.2, 3.0, 2.9 and 2.8 for Charlotte, Birmingham, Columbia, and Oklahoma City, respectively. These values are not much higher than the healthy housing market average of 2.6.

Although the growth rate difference between home prices and household income has accelerated in recent years, they are still lower than those observed in the Western and Northeastern regions. Now might be the time to buy for would-be homeowners living in the South, as home values steadily rise and price-to-income ratios remain reasonable.

The Midwest remains the most affordable option for hopeful buyers

The Midwest includes Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin.

The Midwest represents the only remaining region in the US where the median borrower doesn't need to suffer a serious financial burden. Even in 2017, the overall price-to-income ratio in the Midwest was 2.9, relative to a ratio of 4.2 averaged across the other three regions. Looking at the growth rate over time, we find that while median home prices increased by 82% in the Midwest since the 1960s, median household income increased by 29% since the 1960s.

The discrepancy between the growth rates of home prices and household income has been climbing in the Midwest since the 1990s; however, the discrepancy is not as large as it is in other regions. From 2000 to 2017, median home prices showed an increase of 29%, whereas median household income showed a decrease of 1%. Still, the growth rate difference between home prices and household income in 2017 is half of the difference observed for the Southern region and a quarter of the difference observed for the Western region.

Let's look at three major Midwest metropolitan areas, St. Louis, MO, Des Moines, IA, and Cincinnati, OH, to see why the Midwest is so affordable. In 2017, the price-to-income ratio was 2.8 for St. Louis, and 2.7 for Des Moines and Cincinnati, inline with other healthy housing markets.

During the 1980s, St. Louis and Des Moines household incomes were actually growing faster than home prices. By the early 2000s, this pattern flipped, and home prices began to outscale household incomes. The discrepancy between income and home prices makes home buying still within reach in many major Midwestern metropolitan areas, making the Midwest the most affordable region to buy a home today.

The American Dream is changing with the times

Looking at our data and reported findings, it's no surprise that the American Dream is transforming. According to Massachusetts Mutual Life Insurance’s survey, 71% of respondents use vague terms such as "financial independence" when describing the American Dream rather than equating it with homeownership.

For many Americans, homeownership is completely out of reach, with sky-high rents making it impossible to save for a large down payment. The good news is that there are still many inland cities where homeownership is affordable.

In the West and Northeast, especially in the coastal metros, household income could not keep up with the growth of the housing market over the years. This pattern is especially visible for the Western metros. In the South, homeownership is still affordable; however, if the growth rate gap between home prices and household income continues to widen, home buyers might struggle. The Midwest seems to be the only remaining region where purchasing a house with little financial struggle will be possible at least in the near future.

Methodology

To determine how the cost and affordability of housing changed over time, we gathered data on median home values, median gross rent (monthly), and median household income (yearly). We indexed each measure to 1960 values and compared how these measures changed from 1960 to 2017. We adjusted all of our measures for 2017 inflation, and compared these measures on national, regional, and metropolitan-level for selected areas. Below are the where we gathered each measure from.

1. For the national and regional levels, all measures from 1960 to 2000 are from the Decennial Census.
2. For national and regional levels, all measures for 2008, 2010 and 2017 are from the American Community Survey from the Census Bureau.
3. For the metropolitan level, median home values from 1960 to 2000 are from IPUMS, https://usa.ipums.org/usa. We calculated median home values based on home values of 1-in-100 or 5-in-100 random samples.
4. For metropolitan level, median household income values from 1960 to 2000 are from the Decennial Census.
5. For metropolitan level measures, all measures for 2008, 2010 and 2017 are from the American Community Survey from the Census Bureau.
6. Because of lack of data, we used median family income as a measure of income for 1960 instead of median household income.

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Homeownership is a long-term investment, with the typical mortgage lasting between 15 and 30 years; however, most home buyers don't consider the potential impact of climate change on their most important investment.

The reality is climate change could have a serious impact on how the real estate industry approaches property values and assesses risk.

Although climate change is a global phenomenon, its impact will not be the same across the globe. For instance, while some regions might have to contend with increased flooding, others might experience extreme heat waves or cold snaps.

Therefore, the effect of climate change will vary based on:

1. The type of potential climate-related hazard
2. The vulnerability of a location to that particular type of hazard
3. How the location can adapt to that type of hazard (i.e. emergency readiness)

To understand how climate change will impact major cities across the United States, we gathered and analyzed available public data from Notre Dame Global Adaptation Initiative (ND-GAIN).

This dataset includes risk and readiness scores for extreme climate events (e.g., cold, heat, flood, drought, and sea-level rise) for over 270 U.S. cities, as well as the probability of a climate-related disaster occurring in 2040.

We looked at the 100 most populated cities reported in the dataset and developed a ranking system to identify which cities will be most affected by climate change. We then created an impact metric to look at how each city will be impacted by specific climate change-related disasters.

Here's what you need to know.

Key Findings

• The cities that are most vulnerable to climate change hazards are also the least prepared for them
• Coastal cities have higher risk scores relative to inland cities, meaning they are more vulnerable to climate change-related hazards
• Extreme heat is more likely to impact cities in Florida and the Midwest because they're at higher risk of heat waves and are more vulnerable to relative humidity
• Floods are more likely to impact cities in California and Texas because they are near large river basins
• Eastern and southern coastal cities are more likely to be affected by sea-level rise compared to western coastal cities
• Somewhat counter-intuitively, extreme cold is more likely to negatively impact cities in warmer states like Texas and California because they lack the requisite infrastructure

Terminology

Below are the general terminologies used in our study (you can find the exact definitions provided by ND-GAIN at the end of this study).

• Probability of an extreme climate event: For each city, an extreme climate event in 2040 is determined relative to that city’s historical average between 1950-1999. The threshold for an extreme event is set high by ND-GAIN, making it reasonably difficult to experience such an event.
• Risk: A city’s vulnerability to climate change. The risk score incorporates exposure, sensitivity and adaptive capacity based on the type of hazard.
• Readiness: A city's overall preparedness for a climate-related event. Readiness score is a function of economic, government, and social readiness and does not change based on the type of hazard (i.e., readiness score remains the same for each climate event).

We first present results regarding overall risk and readiness scores. We then examine each climate event separately and provide rankings based on the overall impact climate change will have on major U.S. cities.

City Risk vs. Readiness

Overall risk and readiness scores measure how well cities perform on all of the indicators, irrespective of the type of environmental hazard.

Ideally, we would want to observe a positive correlation between these measures: If a city is highly vulnerable, being prepared would help the city adapt to a climate-related hazard. Unfortunately, we found a significant negative correlation of -0.29 between risk and readiness scores, meaning as risk scores increased, readiness scores decreased.

In other words, the cities that are most vulnerable are also the ones that will be the least prepared for a climate-related disaster.

Cities at highest risk:

• Santa Ana, CA
• Hialeah, FL
• Miami, FL
• Newark, NJ
• Chicago, IL

Cities with highest degree of readiness:

• Madison, WI
• Seattle, WA
• Plano, TX
• Minneapolis, MN
• Raleigh, NC

Cities with lowest degree of readiness:

• Anaheim, CA
• San Bernardino, CA
• Hialeah, FL
• Santa Ana, CA
• Riverside, CA

Largest difference between risk and readiness scores:

• Santa Ana, CA
• Hialeah, FL
• Anaheim, CA
• Miami, FL
• Newark, NJ

Santa Ana, CA ranks as the most vulnerable city in the United States. This means that if a climate hazard occurs in Santa Ana, many people will be adversely affected — especially because Santa Ana has a relatively low adaptive capacity to respond to the impacts of such an event.

Madison, WI ranks as the best-prepared city for extreme climate events in the United States. This means Madison has economic stability to adapt to extreme climate events, and the government system to support these adaptations. Furthermore, Madison has civil engagement and innovative capabilities that allow its residents to adjust to a more volatile environment.

Next, we identified cities with the biggest difference between risk rating and readiness: This metric helps us identify which cities are more vulnerable and less ready to adapt to climate-related events. When we rank the cities based on difference scores, Santa Ana again ranked as the top, followed by Hialeah, Anaheim, Miami, and Newark.

We also compared coastal cities and inland cities in terms of their risk and readiness scores. On average, coastal cities have statistically higher risk scores (49.4) than inland cities (40), meaning that coastal cities are more vulnerable to climate change. However, coastal and inland cities did not differ in their readiness scores.

This means coastal cities are more vulnerable and sensitive to climate change-related events than inland cities, but they show similar levels of preparedness.

Note: High risk does not mean that a climate change event will happen in these cities; rather, these results suggest that if any of them experience an extreme climate event, they will likely be impacted worse than others. To measure the overall impact of climate change, we incorporated ND-GAIN's probability metric to assess the likelihood of a climate disaster occurring.

Defining Our "High-Impact" Metric

For each extreme climate event, we developed a Climate Change Impact Metric to rank cities (i.e., "highest impact").

We first ranked the 100 most populated cities on the three previously introduced metrics: probability of the extreme event, risk, and readiness.

We then weighted the "probability of the extreme event" .6 and "risk score" and "readiness score" .2 each and added those measures together to calculate our Climate Change Impact Metric.

The probability of the extreme event is weighted more because we wanted to prioritize the possibility of the hazard occurring in a particular location. Vulnerability to and preparedness for disasters are important metrics, but the probability of an extreme event should take precedence when assessing the overall impact of climate change to a particular city.

Higher scores on this metric indicate higher impact (e.g., a score of 100 would mean the highest ranking on probability, the highest ranking on risk, the lowest raking on readiness).

Here are the results, where a higher score indicates the greater impact of a particular hazardous weather event on a city.

To provide the full picture, for each hazard we looked at the five cities that will be most impacted, the cities that have the highest probability of having that specific disaster in 2040, and the cities that are at the highest risk.

Because readiness scores do not change based on the climate event, we do not provide further rankings on them (e.g., Madison, WI; Seattle, WA; Plano, TX; Minneapolis, MN; and Raleigh, NC always rank the top 5) .

Heat

The probability of a heat event in 2040 is defined as the annual probability of six consecutive days in which the temperature of each day falls above the 90th percentile of a city’s baseline period between 1950-1999.

Highest impact:

• Hialeah, FL
• Detroit, MI
• Miami, FL
• Cleveland, OH
• Toledo, OH

Highest probability:

• Indianapolis, IN
• Toledo, OH
• Detroit¹, MI
• Cincinnati¹, OH
• St. Petersburg², FL
• Memphis², TN

Highest risk:

• Santa Ana, CA
• Newark, NJ
• Hialeah, FL
• Miami, FL
• Chicago, IL

^{1 These cities both ranked third.}

^{2 These cities both ranked fifth.}

The number of record high temperature events in the U.S. has been increasing as average global temperatures rise. Heat waves cause more fatalities than other natural disasters such as floods, lightning, tornadoes, and hurricanes.

Heat waves are a major consequence of climate change, and we found that cities concentrated in the Midwest and South will be most affected. Heat-related disasters are more likely to occur in these areas due to a combination of higher risk and lower readiness.

For heat hazards, the risk scores were negatively correlated with readiness scores, meaning higher risk cities are also less prepared for heat waves (-0.29).

Why do heat waves hit the Midwest and the Southeast harder than other regions across the U.S.? Simple: humidity.

As humidity increases, our ability to cool ourselves by sweating diminishes. According to the Midwestern Regional Climate Center, the Midwest's hot weather is typically accompanied by high humidity, resulting in a population that's more vulnerable to the rising temperatures. It typically takes a few days of oppressive heat and humidity before a population becomes affected, but climate change will lead to more consecutively hot days that negatively impact Midwestern populations. Older populations are at greater risk: Conditions that cause heat cramps in a 17-year-old may result in heat exhaustion in someone 40 years old.

Flood

The probability of a flood event in 2040 is defined as the annual probability of 5-consecutive days in which precipitation exceeds a city’s baseline period between 1950-1999.

Highest impact:

• St. Petersburg, FL
• Modesto, CA
• Corpus Christi, TX
• Sacramento, CA
• Stockton, CA

Highest probability:

• St. Petersburg, FL
• Modesto³, CA
• San Jose³, CA
• Orlando³, FL
• Fremont³, CA

Highest risk:

• Hialeah, FL
• Miami, FL
• Irving, TX
• Corpus Christi, TX
• Baton Rouge, LA

^{3 These cities all ranked second.}

The U.S. has also witnessed increasing numbers of intense rainfall events over the last 50 years, with a 20% increase. Floodplains are expected to grow by 40-45% over the next 90 years because of climate change.

We found that flooding will have the highest impact on St. Petersburg, FL; Corpus Christi, TX; Modesto, CA; Sacramento, CA; and Stockton, CA.

Southern cities, such as St. Petersburg, FL and Corpus Christi, TX, are close in proximity to river basins that tend to overflow during above-average rainfall as well as melting snow, leading to increased risk.

Cities in California, on the other hand, are susceptible to floods for multiple reasons. California has many valleys that are highly vulnerable to both overflowing rivers, as well as coastal cities that are exposed to high tides.

The areas that are affected by wildfires and drought are also quite vulnerable to floods because the ground cannot absorb water as efficiently after these incidents.

Much like overall risk and readiness ratings, flooding risk scores were negatively correlated with readiness scores (-0.21).

Drought

The probability of a drought event in 2040 is defined based on precipitation data. Forecasts for lower levels of rainfall are estimated based on the size of the deviation from the historical average.

Highest impact:

• Hialeah, FL
• Chicago, IL
• Richmond, VA
• Riverside, CA
• Norfolk, VA

Highest probability:

• Hialeah⁴, FL
• Richmond⁴, VA
• Chicago⁵, IL
• Spokane⁵, WA
• Virginia Beach, VA

Highest risk:

• Santa Ana, CA
• Boston, MA
• Jersey City, NJ
• Washington, DC
• Baltimore, MD

^{4 These cities both ranked first.}

^{5 These cities both ranked third.}

Drought is yet another consequence of climate change led by global warming. In 2012, 81% of the U.S. was under abnormally dry conditions and fifteen of the 20 largest fires initiated by prolonged dry seasons in California have occurred since 2000.

Droughts have been a reality for 20 years in the southwestern US and by 2040, they are expected to impact more eastern regions as well.

Based on our Climate Change Impact Metric, Hialeah, FL; Chicago, IL; Richmond, VA; Riverside, CA; and Norfolk, VA will be highly impacted by droughts by 2040.

For droughts, the risk scores were not statistically related to readiness scores (-0.05). This means that cities with a higher risk of drought were not necessarily more likely to be less prepared.

Sea Level Rise

The projected sea level rise from NOAA’s sea level rise viewer in feet based on an intermediate scenario for 2040.

Highest impact:

• Norfolk, VA
• Baton Rouge, LA
• New Orleans, LA
• Newark, NJ
• Corpus Christi, TX

Highest probability: Baton Rouge⁶, LA, New Orleans⁶ , LA, Houston, TX, Norfolk⁷, VA, Chesapeake⁷, VA.

Highest risk: Jacksonville, FL, Hialeah, FL, Newark, NJ, Chesapeake, VA, Norfolk, VA.

^{6 These cities both ranked first.}

^{7 These cities both ranked fourth.}

Another adverse effect of global warming is sea level rise. The rate of sea level rise in the last two decades is nearly double that of the last century, with 2014 having the highest global annual average of 2.6 inches above the 1993 average.

In the US, 40% of the population lives in highly populated coastal areas.

Among these areas, Norfolk, VA; Baton Rouge, LA; New Orleans, LA; Newark, NJ; and Corpus Christi, TX will likely be the most affected by sea level rise.

Houston, TX did not make the list because it has a low risk score and a relatively high readiness score, whereas Chesapeake, VA has a relatively high readiness score.

For sea level rise, the risk scores were not statistically related to readiness scores (-.11).

Cold

The probability of a cold event in 2040 is defined as the annual probability of six consecutive days in which the temperature falls below the 10th percentile of a city’s baseline period between 1950-1999.

Highest impact:

• Fresno, CA
• Laredo, TX
• Lubbock, TX
• Stockton, CA
• El Paso, TX

Highest probability:

• Lubbock⁸, TX
• Albuquerque⁸, NM
• Portland⁹, OR
• Lincoln⁹, NE
• El Paso¹⁰, TX
• Denver¹⁰, CO
• Aurora¹⁰, CO

Highest risk:

• Santa Ana, CA
• Miami, FL
• Hialeah, FL
• Newark, NJ
• Philadelphia, PA

^{8 These cities both ranked first.}

^{9 These cities both ranked third.}

^{10 These cities all ranked fifth.}

The number of low temperature days has been decreasing in the U.S. since 1950. Nonetheless, since 1990 harsher winters have been observed in the U.S., a trend that is potentially linked to global warming.

Extreme colds (relative to a particular city’s historical average) can disturb economic activities such as farming or fishing and affect vulnerable populations such as the elderly and children.

Based on our Climate Change Impact Metric, Fresno, CA; Laredo, CA; Stockton, CA; Lubbock, TX; and El Paso, TX rank as the top 5 cities that will be influenced by an extreme cold event (relative to their historical average).

Interestingly, many of these cities are in warm areas like southern Texas.

At first blush, these aren't the cities we typically associate with extreme cold. However, given the increased probability of extreme cold, combined with these cities lack of infrastructure built around cold weather (e.g. snowplows, central heating, etc.), they are much more vulnerable to cold weather events.

Furthermore, economic activities in these cities will be impacted more, with Texas already showing the largest crop-yield decrease, 20%, in the U.S. since Arctic warming.

Although cities such as Portland, OR; Lincoln, NE; and Denver, CO also have higher probabilities of an extreme cold event, these cities are not as vulnerable to such an event (i.e., lower risk scores).

For the cold hazard, we also found a significant negative correlation of -0.28 between risk and readiness scores, meaning that the most vulnerable cities are less ready for such an event.

Conclusion

Some U.S. cities, such as Madison, WI and Seattle, WA, will be less impacted by extreme climate events because their population is less vulnerable and they have a higher capacity to adapt to climate change.

Other cities, such as Hialeah, FL; Corpus Christi, TX; and Newark, NJ consistently ranked in the top 5 cities to be impacted by at least two of the listed extreme climate events. Residents of such cities, as well as people who are planning to move to these areas, should start considering these factors now.

Echoing the report of ND-GAIN, these results are not intended to herald the coming apocalypse, but rather to uncover hidden vulnerabilities and encourage a collective proactive response.

While we cannot stop climate change, we can adapt to it by taking precautionary measures and educating the general public about the coming risks.

For questions about this study or the methodology, reach out to Thomas O'Shaughnessy (thomas@movewithclever.com) and Eylul Tekin (eylul@movewithclever.com).

Full Terminology (taken from ND-GAIN’s website)

Risk

A city’s vulnerability to climate change. Risk incorporates exposure, sensitivity and adaptive capacity:

Exposure relates to physical exposure, which means the number of individuals and critical infrastructure exposed to a climate hazard event. Exposure has a geographic and temporal character.

For extreme cold, heat, flooding, drought and sea-level rise, sensitivity refers to the degree to which population of the city is affected by climate hazards. Adaptive capacity refers to the city’s ability to respond to the consequences of climate hazards.

By way of example, drought sensitivity refers to the degree to which economic sectors rely on water-intensive industries (agriculture, water transportation, mining, utilities) and adaptive capacity reflect the ability of the city to manage drought through the management plans, or early warning systems.

Readiness

The capacity of urban society has to mobilize adaptation investments from private sectors and to target investments more effectively. Readiness is a function of economic, governance, and social readiness:

Economic Readiness: The economic condition to support adaptation and to attract adaptation investment.

Governance Readiness: The governance support that enables effective use of adaptation investment.

Social Readiness: The social capacity that facilitates the uptake of the benefits brought about through adaptation investment.

For the technical report and full methodology of ND-GAIN: https://gain.nd.edu/assets/293226/uaa_technical_document.pdf

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