Session 13: Housing and Urban Economics

Is climate change partisanship reflected in residential decisions? Comparing individual properties in the same zip code with similar elevation and proximity to the coast, houses exposed to sea level rise (SLR) are increasingly more likely to be owned by Republicans and less likely to be owned by Democrats. We find a partisan residency gap for even moderately SLR exposed properties of more than 5 percentage points, which has more than doubled over the past six years. Findings are unchanged controlling flexibly for other individual demographics and a variety of granular property characteristics, including the value of the home. Residential sorting manifests among owners regardless of occupancy, but not among renters, and is driven by long-run SLR exposure but not current flood risk. Anticipatory sorting on climate change suggests that households that are most likely to vote against climate friendly policies and least likely to adapt may ultimately bear the burden of climate change. 

Homeowners’ insurance provides households financial protection from climate losses. To improve access and affordability, state regulators impose price controls on insurance companies. Using novel data, we construct a new measure of rate setting frictions for individual states and show that different states exercise varying degrees of price control, which positively correlates with how exposed a state is to climate events. In high friction states, insurers are more restricted in their ability to set rates and adjust rates less frequently and by a lower amount after experiencing climate losses. In part, insurers overcome pricing frictions by cross-subsidizing insurance across states. We show that in response to losses in high friction states, insurers increase rates in low friction states. Over time, rates get disjoint from underlying risk, and grow faster in states with low pricing frictions. Our findings have consequences for how climate risk is shared in the economy and for long-term access to insurance.

We use county panel data to study the dynamic responses of local economies after natural disasters in the U.S. Specifically, we estimate disaster impulse response functions for personal income per capita and a broad range of other economic outcomes, using a panel version of the local projections estimator. In contrast to some recent cross-country studies, we find that disasters increase total and per capita personal income over the longer run (as of 8 years out). The effect is driven initially largely by a temporary employment boost and in the longer run by an increase in average weekly wages. We then assess the heterogeneity of disaster impacts across several dimensions. We find that the longer-run increase in income per capita rises with disaster severity, as measured by monetary damages. Hurricanes and tornados yield longer run increases in income, while floods do not. The longer run increase in income—which has on average become smaller over time—tends to rise with recent disaster experience and is absent for counties with no recent experience. Finally, state-level analyses and estimates of spatial spillovers across counties suggest that, while over the short- to medium-run, the regional and local impacts of disasters on personal income are similar, over the longer run the net regional effect may be negative, in contrast to the positive local effect.

“Right to Buy” (RTB), a large-scale gentrification policy by which incumbent tenants in public housing could buy their properties at heavily-subsidised prices, increased the homeownership rate in  the UK by over 10 percentage points between 1980 and the late 1990s. This paper studies the impact  of childhood exposure to RTB on human capital accumulation, showing that exposure to RTB at birth  significantly improved pupil performance in high stakes exams and likelihood to obtain a degree, while also reducing the likelihood to experience unemployment at the age of 16-18. The crime  reduction generated by RTB in the early 1980s was a key driver of these human capital gains. This is  evidence of a novel means by which gentrification, and housing provision, may contribute to increase  human capital accumulation and improve educational and professional outcomes in low-income  settings. 

Public goods provided by local governments shape many fundamental aspects of life, such as access to education, safety, and neighborhood quality. But how much households value publicly provided goods compared to neighborhood amenities remains unclear. This paper uses a sample of 1.5 million houses in thousands of neighborhoods that straddle local government boundaries to isolate local government valuation. We find that households value access to specific local governments even when comparing homes on opposite sides of the same street but in different governments, suggesting an important role for excludable local public goods. White and Black households show little differential valuation, while Hispanic and especially Asian households exhibit lower and higher valuation, respectively. Local government valuation is mediated through the quality of schooling, free-riding on high-property tax payers, and the quality of peers with whom public goods are consumed.

There are large differences in mean income across US cities, but little is known about the levels of standard of living in each city—defined as the amount of market-based consumption that residents are able to afford. In this paper we provide estimates of standard of living by commuting zone for households in a given income or education group, and we study how they relate to local cost of living. Using a novel dataset, we observe all debit and credit card transactions, check and ACH payments, and cash withdraws of 5% of US households’ in 2014 and use it to measure mean consumption expenditures by commuting zone and income group. To measure local prices, we build income-specific consumer price indices by commuting zone. We uncover vast geographical differences in material standard of living for a given income level. Low income residents in the most expensive commuting zone enjoy a level of consumption that is about half that of low income residents in the most affordable commuting zone. In the second part of the analysis, we endogenize income and estimate the standard of living that low-skill and high-skill households can expect in each US commuting zone, once we account for geographical variation both in cost of living and also in expected income. We find that for college graduates, there is essentially no relationship between consumption and cost of living, suggesting that college graduates living in cities with high costs of living — including the most expensive coastal cities—enjoy a standard of living on average similar to college graduates with the same observable characteristics living in cities with low cost of living— including the least expensive Rust Belt cities. For high school graduates and high school dropouts we find a significant negative relationship between consumption and cost of living, indicating that expensive cities offer lower standard of living than more affordable cities. The differences are quantitatively large: High school dropouts moving from the most to the least affordable commuting zone would experience a 23.5% decline in consumption.

We develop a framework to estimate the economic value of housing regulations, and apply it to a 2016 zoning reform in the city of Sao Paulo, which altered maximum permitted construction to land area at the city-block level. Using a spatial regression discontinuity design, we find that developers swiftly reacted to the reform by filing for more multi-family construction permits in blocks with higher allowable densities. We incorporate these micro-estimates of developer responses to zoning reforms into an equilibrium model of housing supply and demand to estimate the long term impact of zoning changes on construction, house prices, residential location decisions and resident welfare. Supply responses from the reform produce a 1.4 percent increase in the total housing stock, leading to a 0.4-0.9% reduction in prices. Consumer welfare gains increase 4.5-fold once we account for equilibrium changes in the built environment, since higher neighborhood density and newer buildings are features highly valued by households. There is also substantial heterogeneity, with neighborhoods with the largest increases in permitted density receiving more construction (13.8%) and consequently ending up with lower house prices (4.4%). Moreover, college-educated and higher income households gain the most from the reform, because more of those families can now move from the suburbs to the more central parts of the city. However, nomimal house price losses faced by existing homeowners and landlords overshadow all housing consumption gains, which may explain opposition to higher densification in many cities. Finally, counterfactual simulations of more aggressive zoning reforms - e.g. doubling allowed densities - produce much larger welfare gains.

This paper studies the distortionary effects of land-use regulations that preserve farmland from urban sprawl. I exploit a major policy restricting farm-to-urban land conversion in China - the Farmland Red Line Policy - to provide causal evidence on the negative impact of land-use regulation on local development, measured by GDP and population growth. To understand the aggregate impact of the policy, I develop a quantitative spatial equilibrium model that features endogenous land-use decisions. The calibrated model reveals that the welfare of workers would have been 6% higher in 2010 if the policy had not been implemented.

I investigate the effect of property tax changes on individual homeowner mobility and voted tax rates using a panel of individual assessment and sales records in Ohio. I use regulatory stabilization rules that cause changes in individual taxes with no mechanical change in quantity of public goods to examine how changes in a homeowner's tax bill influence sales, foreclosure events, and home equity loan origination. The changes in taxes I observe are driven by changes in relative assessment growth within school districts and allow me to identify the effects of changes in taxes separately from the effects of changing housing wealth. Using a leave-one-out by county random forest regression on assessed values to instrument for tax changes, I find that a $0.10 increase in the price per dollar of services leads to a 5% increase in the likelihood of sale with no change in the likelihood of foreclosure. I also find suggestive evidence of increased voted tax rates at the school district level when the ratio of median to mean taxable value decreases.

We develop new tools for estimating the causal effects of treatments or instruments that combine multiple sources of variation according to a known formula. Examples include treatments capturing spillovers in social and transportation networks, simulated instruments for policy eligibility, and shift-share instruments. We show how exogenous shocks to some, but not all, determinants of such variables can be leveraged while avoiding omitted variables bias. Our solution involves specifying counterfactual shocks that may as well have been realized and adjusting for a summary measure of non-randomness in shock exposure: the average treatment (or instrument) across such counterfactuals. We further show how to use shock counterfactuals for valid finite-sample inference, and characterize the valid instruments that are asymptotically efficient. We apply this framework to address bias when estimating employment effects of market access growth from Chinese high-speed rail construction, and to boost power when estimating coverage effects of expanded Medicaid eligibility.

We examine the application of quantitative spatial models to the growing body of fine spatial data used to study economic outcomes for regions, cities, and neighborhoods. In “granular” settings where people choose from a large set of potential residence-workplace pairs, idiosyncratic choices affect equilibrium outcomes. Using both Monte Carlo simulations and event studies of neighborhood employment booms, we demonstrate that calibration procedures that equate observed shares and modeled probabilities perform very poorly in such settings. We introduce a general-equilibrium model of a granular spatial economy. Applying this model to Amazon’s proposed HQ2 in New York City reveals that the project’s predicted consequences for most neighborhoods are small relative to the idiosyncratic component of individual decisions in this setting. We propose a convenient approximation for researchers to quantify the “granular uncertainty” accompanying their counterfactual predictions.

We identify the existence of a racial gap in housing returns that is an order of magnitude larger than disparities arising from housing costs alone. The returns gap is driven almost entirely by differences in distressed home sales (i.e. foreclosures and short sales). Black and Hispanic homeowners are both more likely to experience a distressed sale and to live in neighborhoods where distressed sales carry larger foreclosure discounts. Higher rates of distressed sales among minorities are driven by pre-existing differences in economic stability and neighborhood sorting. Black and Hispanic homeowners are more likely to default in response to increases in monthly payments, consistent with racial differences in liquid wealth holdings playing a key role in creating the observed disparities. We use quasi-experimental variation in the receipt of mortgage modifications to show that policies that encourage lenders to modify loans when homeowners can no longer afford their mortgages can mitigate gaps in housing returns, particularly if they are targeted towards minority homeowners or neighborhoods.

Motivated by the assessment of racial discrimination in mortgage pricing, we introduce a new methodology for comparing the menus of options borrowers face based on their choices. First, we show how standard regression based approaches for assessing discrimination in menus can lead to misleading and contradictory results. Second, we propose a new methodology that is robust these problems based on relatively weak economic assumptions. More specifically, we use pairwise dominance relationships in choices supplemented by restrictions on the range of plausible menus to define (1) a test statistic for equality in menus and (2) a difference in menus (DIM) metric for assessing whether one group of borrowers would prefer to switch to another group’s menus. Our statistics are robust to arbitrary heterogeneity in borrower preferences across racial groups, are sharp in terms of identification, and can be efficiently computed using Optimal Transport methods. Third, we devise a new approach for inference on the value of Optimal Transport problems based on directional differentiation. Fourth, we use our methodology to estimate mortgage pricing differentials by race on a novel data set linking 2018–2019 Home Mortgage Disclosure Act (HMDA) data to Optimal Blue rate locks. We find robust evidence for mortgage pricing differentials by race, particularly among Conforming mortgage borrowers who are relatively creditworthy.

We develop tests for discrimination that we apply to 25 years of mortgage lending. Our tests limit the scope for omitted variables in a conventional benchmarking test by combining high-frequency mortgage evaluations with the notion that economic incentives can mitigate subjective biases. Loan officers have monthly volume quotas that constrain their subjectivity on loans processed at month-end. Concurrently, applicant characteristics are time-invariant within-month. We estimate that loan officers’ subjectivity contributes to at least half of the unexplained Black approval gap. The within-month approval gap is smaller for shadow banks, but not for FinTech lenders or banks in concentrated markets.

We study the dual role of real-estate investors – households who own multiple housing units – in ownership and rental housing markets in Israel. Exploiting a series of capital gains tax changes and rich administrative data, we first show that real-estate investors that were subject to an unexpected temporary capital-gains tax exemption increased their sales of housing units by 50%. Predominantly, the housing units sold by investors were purchased by single homeowners and were previously occupied by renters. Next, we exploit spatial variation in the share of the housing stock owned by investors across 360 local markets to examine how investors’ activity induced by the tax changes affected local house prices and local rents. We present evidence that a 1 percentage point increase in investors’ sales out of stock led house prices to decrease by 14% and rents of new leases to increase by 4%. These effects are larger for smaller and older units, in which investors own a larger share of the stock of housing units. The results suggest that policies that encourage investors to sell can achieve their stated objective of reducing house prices, but also run the risk of restricting the supply of rental housing, and thus adversely affecting renters.

We study the frictions in dealer-intermediation in residential real estate through the lens of “iBuyers,” technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid and in which automated valuation models have low pricing error. These facts suggest that iBuyers’ speedy offers come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand the economic forces underlying the tradeoffs of dealer intermediation in this market. The model reveals the central tradeoff to intermediating in residential real estate. To provide valuable liquidity service, transactions must be closed quickly. Yet, the intermediary must also be able to price houses precisely to avoid adverse selection, which is difficult to accomplish quickly. Low underlying liquidity exacerbates adverse selection. Our analysis suggests that iBuyers’ technology provides a middle ground: they can transact quickly limiting information loss. Even with this technology, intermediation is only profitable in the most liquid and easy to value houses. Therefore, iBuyers’ technology allows them to supply liquidity, but only in pockets where it is least valuable. We also find limited scope for dealer intermediation even with improved pricing technology, suggesting that underlying liquidity will be an impediment for intermediation in the future.

This paper measures structural vacancies in housing markets with tenure choice.  We first document that (i) inventory for rent and for sale are strongly correlated  across US metro areas and (ii) months supply (inventory relative to monthly  volume), is always larger in rental markets: a renter is faster to find then a buyer. We propose a search model with developers who choose between selling  houses, which yields higher surplus, or renting them out, which allows for faster matching. The estimated model accounts for the facts and allows us  to infer structural vacancies from the behavior of inventory and volume. Structural vacancies in rental markets are negative in many cities even while they are positive in owner occupier markets.

In recent decades, the U.S. labor market has become more unequal and polarized: wage differences have widened and middle-income jobs have been replaced by low and high-income jobs. The rise in inequality and polarization have been more pronounced in large cities. I argue that this can be explained by higher house price growth in big cities, which makes it harder for middle-income households to buy a house there. I build a spatial equilibrium model in which households differ by skill, and choose where to live and whether to rent or own housing. Low-skilled households cannot afford to buy in any location, while the high-skilled can buy anywhere. Meanwhile, the middle-skilled can only buy a house in affordable places. Thus, the middle of the skill distribution in expensive locations empties out, making them more polarized and unequal. Empirical evidence supports these predictions. First, middle-income households are more likely to move to states with lower house prices for housing-related reasons than those with low or high income. Second, polarization and inequality grew more in commuting zones where prices increased the most. Counterfactual experiments show that rising price-wage and price-rent ratios account for 12% to 21% of the increase in low and high-paid jobs and for 7% to 10% of the growth in the variance of log wages in the 20 largest commuting zones since 1980.   

To study the effects of neighborhood and place-based interventions, this paper incorporates neighborhood effects into a general equilibrium (GE) heterogeneous-agent overlapping-generations model with endogenous location choice and child skill development. Importantly, housing costs, as well as neighborhood effects, are endogenously determined in equilibrium. Having calibrated the model using U.S. data, we use simulations to show that predictions from the model match reduced form evidence from experimental and quasi-experimental studies of housing mobility and urban development programs. After this validation exercise, we study the long-run and large-scale impacts of vouchers and place-based subsidies. Both policies result in welfare gains by reducing inequality and generating improvements in average skills and productivity, all of which offset higher levels of taxes and other GE effects. We find that a voucher program generates larger long-run welfare gains relative to place-based policies. Our analysis of transition dynamics, however, suggests there may be more political support for place-based policies.

We develop a dynamic spatial general equilibrium model with forward-looking investment and migration decisions. We characterize analytically the transition path of the spatial distribution of economic activity in response to shocks. We apply our framework to the reallocation of US economic activity from the Rust Belt to the Sun Belt from 1965-2015. We find slow convergence to steady-state, with US states closer to steady-state at the end of our sample period than at its beginning. We find substantial heterogeneity in the effects of local shocks, which depend on capital and labor dynamics, and the spatial and sectoral incidence of these shocks.