DISCUSSION PAPER SERIES
IZA DP No. 15447
Kevin Corinth
Hugo Dante
The Understated ‘Housing Shortage’ in
the United States
JULY 2022
Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may
include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA
Guiding Principles of Research Integrity.
The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics
and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the
world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our
time. Our key objective is to build bridges between academic research, policymakers and society.
IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper
should account for its provisional character. A revised version may be available directly from the author.
Schaumburg-Lippe-Straße 5–9
53113 Bonn, Germany
Phone: +49-228-3894-0
IZA – Institute of Labor Economics
DISCUSSION PAPER SERIES
IZA DP No. 15447
The Understated ‘Housing Shortage’ in
the United States
JULY 2022
Kevin Corinth
Joint Economic Committee and IZA
Hugo Dante
George Mason University
ABSTRACT
IZA DP No. 15447 JULY 2022
The Understated ‘Housing Shortage’ in
the United States
*
Following popular discourse, we abuse economic terminology by defining the “housing
shortage” in the United States as the difference between the number of homes that
would be built in the absence of supply constraints and the actual number of homes. The
magnitude of the housing shortage is important to policymakers, who use it to measure
the scope of the housing supply problem and the extent to which proposed policies
would solve it. However, previous studies understate the housing shortage because they
estimate how many more homes would have been built if historical building or household
formation trends prevailed today, even though historical trends were also affected by
supply constraints. We are the first to use a supply and demand framework to estimate
the full housing shortage in the United States. Using county-level data on land shares of
home prices, we estimate that the U.S. housing shortage was 20.1 million homes in 2021,
14.1 percent of the national housing stock. Our housing shortage estimate is 4 to 5 times
as large as previous estimates, and 13 times as high as the shortage cited by the White
House to contextualize the effects of policies intended to close the gap. Consistent with
predictions of economic theory, our estimated housing shortage is uniformly low in areas
with low regulation but varies in areas with high regulation, since a housing shortage
requires both stringent regulations and strong housing demand.
JEL Classification: R31, R38, R52
Keywords: housing, regulation, land use, supply constraints, shortage
Corresponding author:
Kevin Corinth
Joint Economic Committee
50 Constitution Avenue NE
Washington, DC 20002
USA
E-mail: [email protected]
* The views in this paper reflect those of the authors and should not be attributed to the Joint Economic Committee
or any other institution.
2
I. Introduction
Economists—and increasingly policymakers—recognize that regulatory barriers to housing
development restrict supply, increase home prices, and have negative economic consequences.
Local land use regulations such as minimum lot sizes, height restrictions, occupancy limits,
parking space requirements, and permitting delays impose costs on the development of housing.
When too few homes are built each year and demand grows, prices rise. The extent of the
problem and its variation across geographic regions has been quantified by indices of regulatory
stringency (Ganong and Shoag 2017; Gyourko et al. 2019), the elevated home prices that result
from regulatory barriers (Glaeser and Gyourko 2018), and the resulting shortfall in housing
quantities (e.g., Khater et al. 2021; Rosen et al. 2021; Kingsella and MacArthur 2022). The
extensive economic consequences of regulatory barriers have also been documented. Excessive
regulatory barriers increase home prices (e.g., Quigley and Raphael 2005; Saiz 2010; Albouy and
Ehrlich 2018), suppress economic growth (Glaeser and Gyourko 2018; Hsieh and Moretti 2019),
impede regional economic convergence (Ganong and Shoag 2017), increase homelessness
(Raphael 2010), reduce fertility (Shoag and Russell 2018) and reduce the effectiveness of rental
assistance programs (Ericksen and Ross 2015; Corinth and Irvine 2021).
Policymakers, perhaps convinced by the economists, have increasingly recognized the problem
of supply constraining regulations, and they have sought to implement policies to address it.
Some state legislatures, including California, Utah, Massachusetts, Minnesota, Nebraska, New
York, and New Jersey, have passed reforms that begin to loosen obstacles to new housing
construction, reduce or eliminate density restrictions, and streamline environmental rules
(Karlamangla 2021; Woodruff 2021). At the federal level, the proposed Housing, Opportunity,
Mobility, and Equity (HOME) Act attempts to take a more active federal role in relaxing
exclusionary zoning and density restrictions by making transportation funding contingent on
local deregulatory efforts.
1
President Trump in 2019 signed an executive order “Establishing a
White House Council on Eliminating Regulatory Barriers to Affordable Housing” (White House
2019). President Biden in 2022 proposed a “Housing Supply Action Plan” that would, among
1
“Booker, Clyburn Take Innovative, Two-Pronged Approach to Tackling Affordable Housing Crisis,” October 23,
2019,
https://www.booker.senate.gov/news/press/booker-clyburn-take-innovative-two-pronged-approach-to-
tackling-affordable-housing-crisis.
3
other provisions, incentivize localities to liberalize zoning and land use policies (White House
2022).
In order to understand whether proposed policies will effectively address the housing supply
problem, policymakers require an understanding of its scope. Among the more popular ways for
policymakers to characterize the scope of the housing supply problem is via an estimate of the
“housing shortage.” The housing shortage is intended to express the gap between the number of
homes that would exist absent supply constraining regulations, and the number of homes that
actually exist. Of course, economists do not typically use the term housing shortage to express
the quantity-reducing effects of supply constraints—while prices may be artificially elevated,
any buyer can generally purchase a home at the market price. We nonetheless follow popular
discourse and abuse economic terminology.
The problem with existing estimates of the housing shortage (aside from the improper
terminology) is that they understate the true size of the problem. They build their estimates by
extrapolating historical trends in building or household formation, which simply measure the gap
between current quantities of new housing and the quantities that would be expected based on
historical patterns. This method implicitly assumes that the historical patterns represent the pace
of housing construction or household formation that would be consistent with an unconstrained
housing market. Because land use regulations have existed since at least the 17
th
century and in
their modern form since the 1900s, the assumption that historical trends represent outcomes in an
unrestricted housing market is unlikely to be true.
In this paper, we define a housing shortage in a particular market as the gap between the current
number of homes and the number of homes that would exist absent supply constraining
regulations. Unlike other studies, we use a supply and demand framework to estimate the
housing shortage. Specifically, we use county-level estimates of the land share of home values
from Davis et al. (2021), and following others (e.g., Glaeser and Gyourko 2018) we assume that
in a market without supply constraints that land shares would fall to about 20 percent of the
value of a home. Applying estimates of the price elasticity of demand for housing from the
academic literature, we can then quantity the equilibrium quantity of housing in each market
absent supply constraints.
4
We estimate a national housing shortage of 20.1 million homes, 14.1 percent of the U.S. housing
stock. The housing shortage is the largest in Hawaii (35 percent), the District of Columbia (35
percent), California (31 percent), and Massachusetts (30 percent). Our national housing shortage
estimate is 13 times the 1.5 million estimate cited by the White House to contextualize the scope
of its Housing Supply Action Plan, and between 4 and 5 times the shortage cited in previous
studies. Thus, proposed policies that set out to address a meaningful share of previous estimates
of the housing shortage are likely to fall far short in addressing the full scope of the problem.
We validate the geographic variation in our housing shortage estimates by examining their
relationship with the Wharton Residential Land Use Regulation Index, at the metropolitan area
level. We find that housing shortage estimates are uniformly low for metropolitan areas with
relatively lax regulations, and housing shortage estimates display wide variation in areas with
relatively stringent regulation. Among metropolitan areas in the bottom quartile of the Wharton
index (i.e., with the least stringent regulations), the difference between the 25
th
and 75
th
percentile of the housing shortage (expressed as a percent of the housing stock) is 6.6 percentage
points. Among metropolitan areas in the top quartile of the Wharton index (i.e., with the most
stringent regulations), the difference between the 25
th
and 75
th
percentile of the housing shortage
is 17.3 percentage points. These findings are consistent with theoretical predictions: Quantities
are constrained only when regulations are stringent and demand is strong enough such that those
regulations bind. Thus, stringent regulations are a necessary but not sufficient condition for a
large housing shortage.
Our paper contributes to the economic literature that quantifies the housing supply problem in
the United States and how it varies geographically—which includes indices of regulatory
stringency, elevated prices, and shortfalls in quantities. Our paper is similar in spirit to the
Glaeser and Gyourko (2018) “regulatory tax” that estimates the extent to which home prices
exceed the cost to produce a home. From this price differential, we ask a follow-up question—
how many more homes would be built if this regulatory tax were eliminated? The difference
between the present housing stock and the housing stock inclusive of these new homes represents
what we colloquially refer to as the “housing shortage.”
The paper proceeds as follows. Section II explains the supply and demand framework for
characterizing the housing shortage. Section III describes the model and data used to estimate the
5
housing shortage. Section IV reports results at the national, state and county levels, and validates
geographic variation in estimates at the metropolitan area level using the Wharton Residential
Land Use Regulation Index. Section V concludes.
II. Supply and Demand Framework
Unlike previous studies quantifying the housing shortage, we define the term based on
fundamentals of the market. While a home buyer can in general find a home to purchase at the
market price, the market price can be inflated due to supply constraining regulation. The gap
between the market price of housing under current supply constraining regulations and the
market price of housing if such regulations were relaxed is what Glaeser and Gyourko (2018)
define as a “regulatory tax.” Working from this more fundamental understanding of the market,
we define the housing shortage in a market as the difference between (i) the number of homes
that would be constructed absent supply constraints, and (ii) the actual number of homes in the
market.
Figure 1 graphically represents the housing shortage in a market that is supply constrained.
Demand (black line) is downward sloping because consumers, including from other areas, are
willing to buy more homes when the price falls. The supply curve (solid grey line) is vertical
below the current price
, since housing is a durable good and so quantity supplied does not fall
when prices decline. Supply is upward sloping for higher prices because constraints on building
cause the cost of supplying housing to rise with quantity. Without supply constraints, the price of
housing would fall to the cost to produce a house,
, the sum of the cost of construction, land
value, and a normal profit margin. Glaeser and Gyourko (2018) call this the “minimum profitable
production cost.” Suppliers are willing to provide an unlimited number of homes to the market at
price
, the production cost (dashed grey line). The housing shortage is equal to the equilibrium
number of homes with unconstrained supply,
, minus the equilibrium number of homes with
constrained supply,
.
6
Figure 1. Diagram of a Housing Shortage in a Market
Note: P
0
and Q
0
are the actual housing price and housing quantity, respectively. P
1
and Q
1
are the housing price and
housing quantity in a counterfactual market with unconstrained supply. The difference between the actual and
counterfactual housing supply is defined as the housing shortage.
From Figure 1, we see that the housing shortage is largest in markets where demand is more
elastic, and where the gap between the existing price,
, and the cost to produce housing,
, is
largest. This gap will be largest when onerous regulations produce a steep supply curve and
strong demand bids up prices. Meanwhile, the housing shortage is zero in markets where supply
is not the binding constraint on housing development, either because regulations are not
restrictive or demand is weak.
III. Model and Data
Using the above supply and demand framework, we estimate the housing shortage in each
county in the United States using price elasticity of housing demand estimates from the academic
literature, and county-level estimates of the land-share of home values, which we use to estimate
the differences between observed market prices and the hypothetical prices absent supply
constraints.
We assume that in a housing market without supply constraints, the value of land will comprise
about 20 percent of the total value of the home. This assumption follows Glaeser and Gyourko
7
(2018) who note that an industry rule of thumb is that land values comprise at most 20 percent of
the combined total of land values and construction costs in a market with few building
restrictions.
2
This assumption is also consistent with research by Davis et al. (2021) who show
the relationship between metro-level land-shares and the extent of regulation measured via the
Wharton Residential Land Use Regulation Index. They find metro areas with the least stringent
regulations have land-shares clustering around 20 percent. Thus, relaxing supply constraints in
currently constrained markets can be expected to reduce home prices until land-shares reach 20
percent of the total price of a home.
Letting
denote the land-share of the home price, we can write the price of a home
as
=
+
(
1
)
(1)
where
is the value of the land and
(
1
)
is the value of the structure.
In a market without restrictions on building, the land-share of the home price should be at its
minimum level
(i.e., 20 percent), because otherwise, developers incentivized by the
opportunity to pursue positive economic profits will build more homes (potentially more
densely) until the increased supply reduces home prices to the cost of building a home. We can
express the price of a home in a market after restrictions on building housing have been
removed,
, as
=
+
(
1
)
(2)
The second term
(
1
)
does not change because the value of the structure does not change.
Solving for
, we obtain
=
1
1
(3)
2
This also applies to the market price of the home, as there is an implied minimum level of entrepreneurial profit
required to build a home. In Glaeser and Gyourko (2018) this level was identified as gross margins of approximately
17 percent applied to both land and the structure.
8
Thus, the higher the initial land-share of the home price, the more the home price will fall when
restrictions on building are lifted.
We can also approximate the total number of homes after relaxing restrictions on building
housing by applying estimates from the academic literature of the price elasticity of housing
demand. Rearranging the elasticity formula,
=
%
%
, and using equation (3), we obtain the
number of new homes built when relaxing restrictions.
=
1
1
1
+ 1
(4)
The housing shortage is thus given by
=
1
1
1
(5)
We set the price elasticity of demand for housing,
= 0.7, following central estimates from the
academic literature. For example, Polinsky and Ellwood (1979) estimate an elasticity of about
0.7, and Albouy et al. (2016) estimate an elasticity of around two thirds. Glaeser et al. (2014), for
their own simulations that rely on the relationship between changes in home prices and the
housing stock, note that elasticity estimates are typically near or slightly below one. As noted
previously, we set
= 0.2 following Glaeser and Gyourko (2018) and Davis et al. (2021).
Because we estimate the housing shortage at the county level, we require county-level estimates
of the housing stock
and land-share
. We obtain estimates of
from the American
Community Survey 2016-2020 five-year pooled sample. We update these 2016-2020 average
values to 2021 based on previous growth rates in each county’s housing stock and the observed
national housing stock in 2021.
3
3
We first calculate the difference between (i) the national housing stock in 2021 according to the Census Housing
Inventory estimate, and (ii) the aggregate housing stock observed in the 2016-2020 ACS five-year pooled sample.
We attribute a share of this total increase in the housing stock to each county. The weight for each county is its
9
We obtain land-share estimates from Davis et al. (2021), who publish land-share and structure
value estimates for various geographic designations for each year from 2012 through 2019.
When available, we use the 2019 county-level land-share estimates (which cover 85 percent of
the U.S. population). For the counties for which 2019 data are not available, we use their pooled
estimates which represent an average over the period 2012-2019 (covering an additional 13
percent of the U.S. population), which we update to 2019 based on state-level increases in land-
shares.
4
Land-share values are unavailable for 766 counties, but these counties contain less than
2 percent of the U.S population and are sparsely populated, with only 4.4 people on average per
square mile. Finally, we update the 2019 land-share estimates to 2021 based on metropolitan area
increases in home prices from 2019 to 2021, after netting out the 16.7 percent increase in U.S.
construction prices over this time period.
5
IV. Results
We estimate an aggregate U.S. housing shortage of 20.1 million homes in 2021, 14.1 percent of
the stock of existing homes. As reported in Table 1, our 20.1 million national housing shortage
estimate is several times larger than previous estimates, which relied on different definitions of a
housing shortage. For example, Kingsella and MacArthur (2022) and Khater et al. (2021) both
estimate a shortage of 3.8 million homes, and Rosen et al. (2021) estimate a shortage of 5.5
million homes. The White House reports a housing shortage of just 1.5 million homes. As
compounded annual growth rate of the housing stock based on the 2012-2016 ACS five-year pooled sample and the
2016-2020 ACS five-year pooled sample. U.S. Census Bureau, Housing Inventory Estimate: Total Housing Units in
the United States [ETOTALUSQ176N], retrieved from FRED, Federal Reserve Bank of St. Louis;
https://fred.stlouisfed.org/series/ETOTALUSQ176N, April 28, 2022.
4
We update the 2012-2019 pooled estimates to 2019 by assuming that the percent increase in the land-share in the
county from 2012-2019 until 2019 equals the percent increase in the land-share in the state from 2012-2019 until
2019.
5
We first calculate the 2021 home value (in dollars) for each county by increasing the 2019 home value by its
metropolitan area (using Census Bureau 2022a) percentage change in the Federal Housing Finance Agency’s
(FHFA) All Transactions House Price Index (HPI) (FHFA 2022). In the case that a county did not fall within a
metropolitan area, we applied the state level non-metropolitan area HPI change, following the methodology of NAR
(2022). We then calculate the 2021 structure value for each county by increasing the 2019 structure value obtained
from Davis et al. (2021) by the 16.7 percent increase in U.S. construction prices as measured by the Price Deflator
(Fisher) Index of New Single-Family Houses Under Construction (Census Bureau 2022b). The 2021 land value is
equal to the 2021 home value minus the 2021 structure value, which is then expressed as a share of the total 2021
home value. To validate our adjustment, we estimated the national value of housing stock, following the application
of the FHFA HPI values to counties, and compared our estimate to the 2021Q4 Z.1 Financial Accounts of the United
States from the Federal Reserve. The value of all real estate in Q4 of 2021 as estimated by the Federal Reserve
amounted to $75.4 trillion, while our estimate (limited to only residential real estate) amounted to $63.6 trillion. This
indicates that the remaining commercial real estate would be worth approximately $12 trillion, which is
approximately correct.
10
described earlier, these estimates rely on extrapolating previous market trends, rather than
capturing the entire shortfall in the housing stock due to excessive regulations.
Table 1. Ratio of Housing Shortage Estimate to Housing Shortage Estimates from Previous
Studies
Study
Housing Shortage Definition
Estimate
Year
Estimate
Estimate
Ratio
Corinth and Dante (2022)
Difference between current
housing stock and housing stock
absent supply constraints
2021
20.1 million
1.0
White House (2022)
N/A
N/A
1.5 million
13.4
Kingsella and MacArthur (2022)
Based on household formation
2019
3.8 million
5.3
Khater et al. (2021)
Based on household formation
2020
3.8 million
5.3
Rosen et al. (2021)
Based on previous building trends
2020
5.5 million
3.7
Note: Estimate ratio is the ratio of the housing shortage estimate from this paper (Corinth and Dante 2022) to the
housing shortage estimate from the study in each row. White House (2022) states that its 1.5 million housing
shortage estimate is from Parrott and Zandi (2021). The White House notes: “While estimates vary, Moody’s
Analytics estimates that the shortfall in the housing supply is more than 1.5 million homes nationwide.” While
Parrott and Zandi (2021) do not appear to directly report this 1.5 million home estimate, they note that the housing
supply shortfall is “equal to almost a year of new construction at its current pace.” New privately owned housing
units completed totaled around 1.3 million each year between 2019 and 2021, according to Census data.
In Figure 2, we report the housing shortage in each state as a share of the state’s existing housing
stock. The housing shortage is the largest in heavily regulated coastal markets. The states with
the largest housing shortages as a share of current housing stock are Hawaii (35 percent), the
District of Columbia (35 percent), and California (31 percent). However, some landlocked
western states also have large housing shortages, notably Utah (24 percent), Idaho (19 percent),
Colorado (17 percent), Arizona (17 percent), and Nevada (12 percent).
11
Figure 2. Housing Shortage as Percent of Total Housing Stock, By State
Note: “Housing shortage” defined as difference between the number of homes that would be built in the absence
of supply constraints and the actual number of homes. State housing shortages as percent of total housing stock
aggregated from county level shortage estimates.
Source: U.S. Census Bureau, Davis et al. (2021), and authors’ calculations
In Figure 3, we show housing shortage estimates by county. Unsurprisingly, the housing
shortage is greatest in the Northeast, Coastal California, and Hawaii, consistent with higher
home prices in these areas. Among counties with a population of at least 200,000 people, the
counties with the largest housing shortages are San Mateo County, California (52 percent),
Arlington County, Virginia (47 percent), San Francisco County, California (45 percent), and
Los Angeles County, California (44 percent). Still, other areas have meaningful housing
shortages as well. Of our 3,143 counties, 20 percent, containing 191 million people, have a
housing shortage of at least 10 percent of the current housing stock. In addition, 3 percent of all
counties, containing 64 million people, have a housing shortage of at least 25 percent. Due to
the growth in home prices during the COVID-19 pandemic, the housing shortage is particularly
prevalent in the Western, non-coastal region of the United States. Multiple land-locked Western
states contain counties with housing shortage of at least 25 percent of their housing stock. For
12
example, Utah contains 3 counties with a housing shortage of at least 25 percent, and these
counties contain 57 percent of the state’s population. Colorado also contains 3 counties with a
housing shortage of at least 25 percent, and these counties contain 18 percent of the state’s
population.
Figure 3. Housing Shortage as Percent of Total Housing Stock, By County
Note: “Housing shortage” defined as difference between the number of homes that would be built in the absence of
supply constraints and the actual number of homes. Figure shows county level estimate of housing shortages.
Source: U.S. Census Bureau, Davis et al. (2021), and authors’ calculations
As a validation of geographic variation in our housing shortage estimates, Figure 4 plots how
metropolitan housing shortages vary with their Wharton Residential Land Use Regulation Index
(Gyourko et al. 2019). This index measures the stringency of land use regulations that impede
new residential construction, where lower values represent fewer restrictions. We expect less
regulated places to have smaller housing shortages. This is what we find. Among metropolitan
areas in the bottom quartile of the Wharton index (i.e., with the least stringent regulations), the
median housing shortage (expressed as a percent of the housing stock) is 2.4 percent. Among
13
metropolitan areas on the top quartile of the Wharton index, (i.e., with the most stringent
regulations), the median housing shortage is 9.2 percent.
Figure 4: Housing Shortage as Percent of Housing Stock and Wharton Residential Land
Use Regulation Index by Metropolitan Area
Note: “Housing shortage” defined as difference between the number of homes that would be built in the absence of
supply constraints and the actual number of homes. Housing shortages as percent of total housing stock calculated at
the Census Core-Based Statistical Area (CBSA) level by using a CBSA to Federal Information Processing Series
(FIPS) County Crosswalk. Wharton Land Use Regulatory Index Values from Gyourko et al. (2019).
Source: NBER, Davis et al. (2021), U.S. Census, Gyourko et al. (2019), and authors’ calculations
We also expect more regulated places to have a wider dispersion of housing shortages: If
demand is weak, then housing shortages should be small and if demand is strong, then housing
shortages should be large. This “fanning-out” pattern is apparent in Figure 4. Among
metropolitan areas in the bottom quartile of the Wharton index, the difference between the 25
th
and 75
th
percentile of the housing shortage is 6.6 percentage points. Among metropolitan areas in
the top quartile of the Wharton index, the difference between the 25
th
and 75
th
percentile of the
housing shortage is 17.3 percentage points. These findings are consistent with theoretical
14
predictions: Quantities are constrained only when regulations are stringent and demand is strong
enough such that those regulations bind. Thus, stringent regulations are a necessary but not
sufficient condition for a large housing shortage.
Notably, the housing shortage is not zero in less regulated places. This is because classification
as “lightly regulated,” based on a low value of the Wharton land use index, does not imply that a
jurisdiction is unregulated or that there is an absence of land use controls that restrict supply
within that jurisdiction. The index authors note that even among lightly regulated areas, approval
for any project generally must pass through at least two entities (usually councils and
commissions) and that almost all of these communities have density restrictions. Ninety-four
percent of these communities have minimum lot size requirements, and the average timespan for
approval of a project is 3.4 months. Housing supply is restricted almost universally in the United
States, indicating that shortages are likely to persist even in some of the least regulated housing
markets. Thus, it should not be surprising that some metro areas with low values of the Wharton
land use index nonetheless have modest housing shortages.
IV. Conclusion
Restrictions on housing supply have a negative impact on the economy and the wellbeing of
American families by driving up the cost of homes in the United States. Rising home prices
impose obstacles on family formation, price workers out of labor markets, dampen economic
growth, and worsen the problems associated with housing insecurity. In order to address these
challenges, policymakers require an accurate understanding the scope of the housing supply
problem. Our 20.1 million housing shortage estimate is 4 to 5 times as large as previous
estimates, and 13 times the housing shortage estimate relied upon by the White House to assess
its 2022 policy proposal to address the housing supply problem. Our results show that the scope
of the problem is far larger and more widespread than policymakers currently recognize, and
thus, that proposed solutions are likely to fall short of solving the problem.
15
References
Albouy, David, Gabriel Ehrlich, and Yingyi Liu. 2016. “Housing demand, cost-of-living
inequality, and the affordability crisis,” National Bureau of Economic Research Working Paper
Series no. 22816, https://www.nber.org/papers/w22816.
Albouy, David, and Gabriel Ehrlich. 2018. “Housing productivity and the social cost of land-use
restrictions.” Journal of Urban Economics 107: 101-120.
Census Bureau. 2022a. “Census Core-Based Statistical Area (CBSA) to Federal Information
Processing Series (FIPS) county crosswalk.” Accessed via National Bureau of Economic
Research, April 29, 2022. https://www.nber.org/research/data/census-core-based-statistical-area-
cbsa-federal-information-processing-series-fips-county-crosswalk.
Census Bureau. 2022b. “Construction Price Indexes.” Accessed March 11, 2022.
https://www.census.gov/construction/cpi/.
Corinth, Kevin, and Amelia Irvine. 2021. “The effect of relaxing local housing market
regulations on the number of recipients of federal rental housing assistance programs.” SSRN
Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3907645
Davis, Morris A., William D. Larson, Stephen D. Oliner, Jessica Shui. 2021. “The price of
residential land for counties, ZIP codes, and census tracts in the United States,” Journal of
Monetary Economics 118: 413-431. https://doi.org/10.1016/j.jmoneco.2020.12.005.
Ericksen, Michael D., and Amanda Ross. 2015. “Housing vouchers and the price of rental
housing.” American Economic Journal: Economic Policy 7(3): 154-176.
Federal Housing Finance Agency (FHFA). 2022. “House Price Index.” Available at
https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index.aspx
Ganong, Peter, and Daniel Shoag. 2017. “Why has regional income convergence in the U.S.
declined?” Journal of Urban Economics 102: 76-90.
16
Glaeser, Edward and Joseph Gyourko. 2018. “The economic implications of housing supply.”
Journal of Economic Perspectives 32(1): 3-30.
Glaeser, Edward, Joseph Gyourko, Eduardo Morales, and Charles G. Nathanson. 2014. “Housing
dynamics: An urban approach.” Journal of Urban Economics 81: 45-56.
Gyourko, Joseph, Jonathan Hartley, and Jacob Krimmel. 2019. “The local residential land use
regulatory environment across U.S. housing markets: Evidence from a new Wharton index.”
National Bureau of Economic Research Working Paper Series no. 265573.
https://www.nber.org/papers/w26573.
Hsieh, Chang-Tai, and Enrico Moretti. 2019. “Housing constraints and spatial misallocation.”
American Economic Journal: Macroeconomics 11(2): 1-39.
Karlamangla, Soumya. 2021. “California’s new housing laws: Here’s what to know.” The New
York Times, Updated October 8, 2021, https://www.nytimes.com/2021/09/20/us/california-
housing-laws.html.
Khater, Sam, Len Kiefer, and Venkataramana Yanamandra. 2021. “Housing supply: A growing
deficit.” Freddie Mac Economic and Housing Research Note.
https://www.freddiemac.com/research/insight/20210507-housing-supply
Kingsella, Mike, and Leah MacArthur. 2022. “Housing underproduction in the U.S.” Up for
Growth. https://www.upforgrowth.org/sites/default/files/2022-
07/UFG_Underproduction_Report_Pages.pdf
National Association of Realtors (NAR). 2022. “Methodology: Median home value and monthly
mortgage payment.” Available at https://www.nar.realtor/research-and-statistics/housing-
statistics/county-median-home-prices-and-monthly-mortgage-payment/methodology-median-
home-value-and-monthly-mortgage-payment.
Polinsky, Mitchell, and David T. Ellwood. 1979. “An empirical reconciliation of micro and
grouped estimates of the demand for housing.” Review of Economics and Statistics 61(2): 199-
205.
17
Parrott, Jim, and Mark Zandi. 2021. “Overcoming the nation’s daunting housing supply
challenge.” Moody’s Analytics. https://www.moodysanalytics.com/-
/media/article/2021/Overcoming-the-Nations-Housing-Supply-Shortage.pdf
Quigley, John M., and Steven Raphael. 2005. “Regulation and the high cost of housing in
California.” American Economic Review 95(2): 323-328
Raphael, Steven. 2010. “Homelessness and housing market regulation.” In How to House the
Homeless edited by Brendan O’Flaherty and Ingrid Gould-Ellen.
Rosen, Kenneth T., David Bank, Max Hall, Scott Reed, and Carson Goldman. 2021. “Housing is
critical infrastructure: Social and economic benefits of building more housing.” Rosen
Consulting Group for the National Association of Realtors.
https://cdn.nar.realtor/sites/default/files/documents/Housing-is-Critical-Infrastructure-Social-
and-Economic-Benefits-of-Building-More-Housing-6-15-2021.pdf
Saiz, Albert. 2010. “The geographic determinants of housing supply.” Quarterly Journal of
Economics 125(3): 1253-1296.
Shoag, Daniel, and Lauren Russell. 2018. “Land use regulations and fertility rates.” In One
Hundred Years of Zoning and the Future of Cities edited by Amnon Lehavi, Springer.
White House. 2019. “Executive order establishing a White House council on eliminating barriers
to affordable housing.” Executive Orders. June 25.
https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-establishing-white-
house-council-eliminating-regulatory-barriers-affordable-housing/.
White House. 2022. “President Biden announces new actions to ease the burden of housing
costs.” White House Briefing Room. May 16. https://www.whitehouse.gov/briefing-
room/statements-releases/2022/05/16/president-biden-announces-new-actions-to-ease-the-
burden-of-housing-costs/.
Woodruff, Daniel. 2021. “Utah lawmakers limit ability of cities and counties to regulate design,
style of housing.” KUTV, May 19. https://kutv.com/news/utah-legislature-2021/utah-lawmakers-
limit-ability-of-cities-and-counties-to-regulate-design-style-of-housing.
18
Appendix
Appendix Table 1. Housing Shortage by State
State
Housing stock
Housing shortage
Housing shortage as share
of housing stock
Alabama
2,325,469
128,405
5.5%
Alaska
326,127
15,440
4.7%
Arizona
3,157,085
524,854
16.6%
Arkansas
1,414,756
61,536
4.3%
California
14,490,486
4,550,097
31.4%
Colorado
2,439,307
424,742
17.4%
Connecticut
1,543,598
336,034
21.8%
Delaware
457,383
49,000
10.7%
District of Columbia
331,500
116,212
35.1%
Florida
9,929,079
1,941,523
19.6%
Georgia
4,484,280
364,666
8.1%
Hawaii
561,510
198,177
35.3%
Idaho
783,002
147,935
18.9%
Illinois
5,426,513
384,336
7.1%
Indiana
2,968,664
186,371
6.3%
Iowa
1,450,405
38,778
2.7%
Kansas
1,308,224
54,494
4.2%
Kentucky
2,033,136
53,110
2.6%
Louisiana
2,133,112
54,403
2.6%
Maine
762,554
108,551
14.2%
Maryland
2,497,802
432,632
17.3%
Massachusetts
2,978,465
886,598
29.8%
Michigan
4,667,018
427,370
9.2%
Minnesota
2,523,007
302,139
12.0%
Mississippi
1,362,833
25,125
1.8%
Missouri
2,862,403
77,813
2.7%
Montana
535,639
63,677
11.9%
Nebraska
871,043
38,425
4.4%
Nevada
1,328,285
159,037
12.0%
New Hampshire
653,684
163,950
25.1%
New Jersey
3,667,351
751,732
20.5%
19
New Mexico
969,833
57,068
5.9%
New York
8,509,070
1,527,798
18.0%
North Carolina
4,900,312
472,699
9.6%
North Dakota
406,236
645
0.2%
Ohio
5,275,671
479,832
9.1%
Oklahoma
1,780,791
85,115
4.8%
Oregon
1,861,874
400,792
21.5%
Pennsylvania
5,810,894
311,242
5.4%
Rhode Island
474,138
131,634
27.8%
South Carolina
2,433,269
183,053
7.5%
South Dakota
416,180
14,439
3.5%
Tennessee
3,107,876
280,613
9.0%
Texas
11,758,527
1,183,783
10.1%
Utah
1,189,946
291,120
24.5%
Vermont
346,495
35,434
10.2%
Virginia
3,627,244
497,539
13.7%
Washington
3,313,614
847,296
25.6%
West Virginia
901,048
4,350
0.5%
Wisconsin
2,761,578
209,415
7.6%
Wyoming
287,681
12,596
4.4%
United States
142,406,000
20,093,625
14.1%
Note: State housing shortages as percent of total housing stock aggregated from county level shortage estimates.
“Housing shortage” defined as difference between the number of homes that would be built in the absence of supply
constraints and the actual number of homes. State housing shortages as percent of total housing stock aggregated
from county level shortage estimates.
Source: U.S. Census Bureau, Davis et al. (2021), and authors’ calculations
