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Land Use Regulations, Migration and Rising House Price Dispersion in the U.S.
... Within the economics literature, a subset of research from the US has pointed to increasing spatial divergences in housing prices. Van Nieuwerburgh and Weill (2010) modelled the distribution of house prices across US metropolitan regions from 1975 to 1994 and show increasing differentiation, with Cun and Pesaran (2018) finding similar increases in price dispersion from 1976 to 2014. Another US study, Gyourko et al. (2013) pointed to growing spatial differences in average house prices over a longer period from 1950 to 2000, particularly driven by select prime localities (what they term 'superstar' cities). ...
... This points to a structural and significant development in ongoing housing market spatial polarization across Spain and echoes findings in comparable research carried out in The Netherlands Hochstenbach and Arundel, 2020), alongside some more limited assessments across the US and other OECD countries (i.e. Cun and Pesaran, 2018;Gyourko et al., 2013;OECD, 2023a). Sorting census tracts by housing values, we find that not only did areas with higher starting values experience greater absolute gains, but they also saw higher rates of appreciation over the period. ...
Housing wealth is central to structuring inequalities across societies. Processes of financialization have intensified the speculative nature of housing, while labour and welfare restructuring increase the importance of property wealth towards economic security. Housing, however, represents an exceptional asset given its inherently spatial nature and buy-in barriers. This implies that not only access to homeownership but where households enter the housing market is central to wealth trajectories. Spatial inequality in housing trends thus fundamentally structures wealth dynamics. While some scholarship has posited increasing housing market spatial polarization, there remains a lack of empirical evidence. This research turns to the context of Spain, to directly assess spatial polarization in housing value accumulation. Employing an innovative dataset at a detailed geographic scale, the analyses reveal strong increases in polarization across the national territory over the past decade. Strikingly, these dynamics appear resistant to major upheavals, including the post-GFC crash and Covid-19 impacts, and are robust across scales. The analyses reveal that more expensive areas saw greater absolute gains and higher rates of appreciation. The findings expose a structural intensification of spatial polarization and provide crucial empirical evidence of how the housing market acts in amplifying inequality through the spatial sorting of wealth accumulation.
... Hsieh and Moretti (2017) estimated that without housing supply constraints, US economic growth would have been considerably higher during the past 50 years. Other authors argue that deregulation of land use can lead to both population reallocations to highly productive cities and improvements in average labor productivity (Cun and Pesaran 2018). ...
... Our results suggest that sorting as a result of land use regulation in high-growth communities is not restricted to the lowest wage earners that have been associated with out-migration. Other authors have theorized that land use regulations distort the distribution of low-wage workers away from high-growth regions (e.g., Cun and Pesaran (2018), Hsieh and Moretti (2017)), but they also may produce distortion within a metropolitan area as well. Further study of the impact of land use regulation on patterns of within-metropolitan migration is warranted. ...
By increasing housing prices, land use regulations can have positive impacts among homeowners, but they can also have negative impacts on the availability of affordable housing. We examined heterogeneity in the price impacts of land use regulation across the conditional house price distribution; this heterogeneity may ameliorate or exacerbate the impacts of land use regulation on affordable housing. Our results suggest that the distribution of price impacts across the conditional house price distribution is relatively uniform. Results suggest that land use regulations both constrain housing supply and induce within housing market migration that spreads price impacts throughout the region.
... Over the last 50 years, an expressed dependence of the city development trajectory (employment dynamics, nominal wages, housing prices, housing supply, the migration inflow, and the population) on the rigidity of construction regulation has formed in the United States (Hilber, Rouwendal, & Vermeulen, 2014;Zabel, 2009). The dispersion of housing prices and the level of labour market integration are significantly affected by the regulation regime of land and real estate markets in the largest cities and MSA (Cun & Pesaran, 2018;Ganong & Shoag, 2017;Gyourko, Mayer, & Sinai, 2013) through housing prices (Modestino & Dennett, 2012;Plantinga, Détang-Dessendre, Hunt, & Piguet, 2013). The influence of housing price affordability on migration from rural to urban areas has also been shown for China (Garriga et al., 2017;Xin & Weihua, 2016). ...
The mutual influence of interregional migrations, the housing market, and the spatial transformation of the Moscow metropolitan area are studied empirically using the database of housing development projects and the depersonalized database of the residence addresses of buyers in the primary housing market. We consider the influence of natural rent, agglomeration economy, interregional inequality, and distance on the differentiation of the activity of residents of various Russian regions and cities in the primary housing market of the Moscow metropolitan area compared to their migration activity. The main characteristics of the extensive development of the Moscow metropolitan area are the prevalence of large greenfield residential district projects, urban sprawl in the near suburban zone, and the concentration of construction along transport corridors in the middle suburban zone. Selective influence of buyers from other regions on various zones of the Moscow metropolitan area owes to their orientation at the most affordable proposals in the primary market. The near suburban zone is the main gate into the Moscow metropolitan area for migrants, and construction in this zone is a major mechanism of restricting housing prices and maintaining economic incentives for large‐scale migration to the Moscow region. Proceeding from the generalization of factors of the spatial transformation of the Moscow metropolitan area, a conceptual model of interaction between migration and urban spatial shifts is proposed.
This paper considers the estimation and inference of spatial panel data models with heterogeneous spatial lag coefficients, with and without weakly exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of the spatial coefficients are derived. The QML estimators of individual spatial coefficients, as well as their mean group estimators, are shown to be consistent and asymptotically normal. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations and results are in line with the paper's key theoretical findings even for panels with moderate time dimensions and irrespective of the number of cross section units. A detailed empirical application to U.S. house price changes during the 1975‐2014 period shows a significant degree of heterogeneity in spatio‐temporal dynamics over the 338 Metropolitan Statistical Areas considered.
We develop a dynamic trade model with spatially distinct labor markets facing varying
exposure to international trade. The model captures the role of labor mobility frictions, goods mobility frictions, geographic factors, and input-output linkages in determining equilibrium allocations. We show how to solve the equilibrium of the model and take the model to the data without assuming that the economy is at a steady state and without estimating productivities, migration frictions, or trade costs, which can be diff cult to identify. We calibrate the model to 22 sectors, 38 countries, and 50 U.S. states. We study how the rise in China's trade for the period 2000 to 2007 impacted U.S. households across more than a thousand U.S. labor markets distinguished by sector and state. We find that the China trade shock resulted in a reduction of about 0.55 million U.S. manufacturing jobs, about 16% of the observed decline in manufacturing employment from 2000 to 2007. The U.S. gains in the aggregate but, due to trade and migration frictions, the welfare and employment effects vary across U.S. labor markets. Estimated transition costs to the new long-run equilibrium are also heterogeneous and reflect the importance of accounting for labor dynamics.
We quantify how sensitive is migration by star scientists to changes in personal and business tax differentials across states. We uncover large, stable, and precisely estimated effects of personal and corporate taxes on star scientists' migration patterns. The long-run elasticity of mobility relative to taxes is 1.8 for personal income taxes, 1.9 for state corporate income tax, and-1.7 for the investment tax credit. While there are many other factors that drive when innovative individuals and innovative companies decide to locate, there are enough frms and workers on the margin that state taxes matter.
The welfare effects of trade shocks turn on the nature and magnitude of the costs workers face in moving between sectors. Using an Euler-type equilibrium condition derived from a rational expectations model of dynamic labor adjustment, we estimate the mean and variance of workers' switching costs from the US CPS. We estimate high values of both parameters, implying slow adjustment of the economy and sharp movements in wages in response to trade shocks. However, import-competing workers can still benefit from tariff removal; liberalization lowers their wages in the short and long run but raises their option value.(JEL E24, F13, F16)
Unemployment inflows fell from 4 percent of employment per month in the early 1980s to 2 percent by the mid 1990s. Using low frequency movements in industry-level data, we estimate that a 1 percentage point drop in the quarterly job destruction rate lowers the monthly unemployment inflow rate by 0.28 points. By our estimates, declines in job destruction intensity account for 28 (55) percent of the fall in unemployment inflows from 1982 (1990) to 2005. Slower job destruction accounts for similar fractions of long-term declines in the rate of unemployment. (JEL E24, E32, J64)
Differences in local fiscal conditions generate compensating differentials across local land and labor markets just as the authors have known amenities to do. Thus, the local fiscal climate affects the quality of life across metropolitan areas. The authors present new results showing that intercity fiscal differentials are nearly as important as amenity differentials in determining the quality of life across urban areas. The paper also investigates the sensitivity of the quality-of-life rankings with respect to assumptions about the nature of the marginal entrant. The authors estimate a random effects model to account for city-specific error components in the housing and wage regressions. Copyright 1991 by University of Chicago Press.
This paper was presented at the conference "Policies to Promote Affordable Housing," cosponsored by the Federal Reserve Bank of New York and New York University's Furman Center for Real Estate and Urban Policy, February 7, 2002. It was part of Session 2: Affordable Housing and the Housing Market.
Implicit markets capture compensation for intraurban and interregional differe nces in amenities and yield differences in housing prices and wages. These pecuniary differences become preference-based weights in a qual ity-of-life index. Hedonic equations are estimated using microdata fr om the 1980 Census and assembled county-based amenity data on climati c, environmental, and urban conditions. Ranking of 253 urban counties reveals substantial variation within, as well as among, the 185 urba n areas. The quality-of-life differences across counties within one S MSA is almost one-half of the difference between the top- and bottom- ranked counties in the nation. Copyright 1988 by American Economic Association.
During the three-year period ending in July 2003, the rise in housing costs in California far exceeded the national inflation rate. Housing prices in five coastal counties increased by more than 60 percent. For the highest quintile of cities, prices increased by an average of more than thirty percent per year. Evidently California housing markets differ along important dimensions from those in the rest of the country. One striking difference is the degree of regulation governing land use and residential construction. California represents the most extreme example of autarky in land use regulations of any U.S. state. Cities are free to set their rules independently, with little oversight. Moreover, state tax policy creates incentives that are likely to decrease production an increase housing costs. Property taxes are constitutionally limited to one percent of acquisition costs while cities are permitted a share of local sales tax receipts. This creates a regulatory incentive to favor retail development over housing construction, to favor development of expensive housing over moderately priced housing, and to discourage the construction of housing.In this paper, we explore the linkages between land-use regulations, growth in the housing stock, and housing prices in California cities. First, we assess whether housing is more expensive in more regulated cities. Next, we assess whether growth in the housing stock over the period of a decade depends on the degree of land-use regulation at the start of the decade. Finally, we estimate the price elasticity of housing supply in regulated and relatively unregulated cities. Our results suggest that current regulations have powerful effects on housing outcomes.
One of the striking features of the last U.S. housing boom was the heterogeneity in the timing of its onset across local markets. In this paper, we exploit this heterogeneity to estimate the extent to which the boom was spread via spatial spillovers from one market to another. Our analysis focuses on spillovers that occur around the time that a local market enters its boom, which we identify using sharp structural breaks in house price growth rates. On the extensive margin, there is evidence that the likelihood of a market booming increases substantially if nearby neighbors boom. On the intensive margin, we also find statistically significant but economically modest effects of the size of a neighbor's boom on subsequent price growth in nearby markets. These affects appear to be unrelated to local market fundamentals, suggesting a potential role for non-rational factors.
We use metro-level variation in land and structural input prices to test and estimate a housing cost function with differences in local housing productivity. Both OLS and IV estimates imply that stringent regulatory and geographic restrictions substantially increase housing prices relative to land and construction input costs. The typical cost share of land is one-third, and substitution between inputs is inelastic. A disaggregated analysis of regulations finds state-level restrictions are costlier than local ones and provides a Regulatory Cost Index (RCI). Housing productivity falls with city population. Typical land-use restrictions impose costs that appear to exceed quality-of-life benefits, reducing welfare on net.
The rate of job loss has been on a secular decline for the last four decades or longer. Changes in demographics or industry composition do not account for the trend. This paper seeks to identify the possible sources of the decline using a simple search-and-matching model with two types of workers, experienced and inexperienced, where the former type faces a risk of skill loss during unemployment. The calibrated model suggests that a higher risk of skill loss during unemployment results in a lower job separation rate, because workers accept lower wages in exchange for job security. Various other potential hypotheses are also examined.
This paper studies the impact of state-level land-use restrictions on U.S. economic activity, focusing on how these restrictions have depressed macroeconomic activity since 2000. We use a variety of state-level data sources, together with a general equilibrium spatial model of the United States to systematically construct a panel dataset of state-level land-use restrictions between 1950 and 2014. We show that these restrictions have generally tightened over time, particularly in California and New York. We use the model to analyze how these restrictions affect economic activity and the allocation of workers and capital across states. Counterfactual experiments show that deregulating existing urban land from 2014 regulation levels back to 1980 levels would have increased US GDP and productivity roughly to their current trend levels. California, New York, and the Mid-Atlantic region expand the most in these counterfactuals, drawing population out of the South and the Rustbelt. General equilibrium effects, particularly the reallocation of capital across states, account for much of these gains.
Cities exist because of the productivity gains that arise from clustering production and workers, a process called agglomeration. How important is agglomeration for aggregate growth? This paper constructs a dynamic stochastic general equilibrium model of cities and uses it to estimate the effect of local agglomeration on aggregate growth. We combine aggregate time-series and city-level panel data to estimate the model's parameters via generalized method of moments. The estimates imply a statistically and economically significant impact of local agglomeration on the growth rate of per capita consumption, raising it by about 10%.
An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross-unit averages to extract common factors (viewed as a source of strong cross-sectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de-factored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatio-temporal model for the de-factored real house price changes and obtain significant evidence of spatial connections, both positive and negative. Copyright
Low-skill workers are comparatively immobile: when labor demand slumps in a city, low-skill workers are disproportionately likely to remain to face declining wages and employment. This paper estimates the extent to which (falling) housing prices and (rising) social transfers can account for this fact using a spatial equilibrium model. Nonlinear reduced form estimates of the model using U.S. Census data document that positive labor demand shocks increase population more than negative shocks reduce population, this asymmetry is larger for low-skill workers, and such an asymmetry is absent for wages, housing values, and rental prices. GMM estimates of the full model suggest that the comparative immobility of low-skill workers is not due to higher mobility costs per se, but rather a lower incidence of adverse labor demand shocks.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
I process satellite-generated data on terrain elevation and presence of water bodies to precisely estimate the amount of developable
land in U.S. metropolitan areas. The data show that residential development is effectively curtailed by the presence of steep-sloped
terrain. I also find that most areas in which housing supply is regarded as inelastic are severely land-constrained by their
geography. Econometrically, supply elasticities can be well characterized as functions of both physical and regulatory constraints,
which in turn are endogenous to prices and demographic growth. Geography is a key factor in the contemporaneous urban development
of the United States.
Examínase en este estudio el movimiento de personas de una división del censo a otra entre 1955 y 1960 como proceso estocástico,
Utilizando la teoria de las cadenas de Markov para construir las probabilidades observadas de movimiento desde cada division
a todas las demás, el estudio computa en seguida las distribuciones de equilibrio de las poblaciones blanca y de color de
las nueve divisiones del censo. Despues de alcanzar un estado de equilibrio, se supone que la población: de cada división
se mantendrá sin cambios . La teoria de las cadenas de Markov es útil en el estudio de la movilidad geográfica a largo plazo;
sin embargo, se hacen necesarias algunas modijicaciones específicas.
Traditional explanations for why some communities block new housing construction focus on incumbent home owner incentives to block entry. Local resident political ideology may also influence community permitting decisions. This paper uses city level panel data across California metropolitan areas from 2000 to 2008 to document that liberal cities grant fewer new housing permits than observationally similar cities located within the same metropolitan area. Cities experiencing a growth in their liberal voter share have a lower new housing permit growth rate.
This paper investigates the effects of land use regulation restrictiveness on house and vacant land prices. In contrast to prior studies, the index of restrictiveness is treated as an endogenous variable and estimated effects are allowed to vary by market setting. Using data on more than 100 Florida cities, greater regulation restrictiveness is found to increase house price and decrease land price. Evidence is also provided showing that more restrictiveness increases the size of newly constructed homes.
This paper provides an empirical analysis of changes in real house prices in the USA using State level data. It examines the extent to which real house prices at the State level are driven by fundamentals such as real per capita disposable income, as well as by common shocks, and determines the speed of adjustment of real house prices to macroeconomic and local disturbances. We take explicit account of both cross-sectional dependence and heterogeneity. This allows us to find a cointegrating relationship between real house prices and real per capita incomes with coefficients (1,−1), as predicted by the theory. We are also able to identify a significant negative effect for a net borrowing cost variable, and a significant positive effect for the State level population growth on changes in real house prices. Using this model we then examine the role of spatial factors, in particular, the effect of contiguous states by use of a weighting matrix. We are able to identify a significant spatial effect, even after controlling for State specific real incomes, and allowing for a number of unobserved common factors. We do, however, find evidence of departures from long run equilibrium in the housing markets in a number of States notably California, New York, Massachusetts, and to a lesser extent Connecticut, Rhode Island, Oregon and Washington State.
New evidence from the 1980, 1990, and 2000 Decennial Census of Housing indicates that expenditure shares on housing are constant over time and across U.S. metropolitan statistical areas (MSA). Consistent with this observation, we consider a model in which identical households with Cobb-Douglas preferences for housing and non-housing consumption choose a location and locations differ with respect to income earned by their residents. The model predicts that the relative price of housing of any two MSAs disproportionately reflects differences in incomes of those MSAs and is independent of housing supply in each MSA. According to the predictions of our calibrated model, the dispersion of rental prices across low- and high- wage MSAs should be larger than we observe: High-wage MSAs like San Francisco are puzzlingly inexpensive relative to low-wage MSAs like Pittsburgh. (Copyright: Elsevier)
We model residential land use constraints as the outcome of a political economy game between owners of developed and owners of undeveloped land. Land use constraints benefit the former group (via increasing property prices) but hurt the latter (via increasing development costs). More desirable locations are more developed and, as a consequence of political economy forces, more regulated. Using OLS as well as an IV approach that directly follows from our model we find strong and robust support for our predictions at the US metro area level. We conclude from our analysis that land use regulations are suboptimal.
This study focuses on the role of wages and rents in allocating workers to locations with various quantities of amenities. The theory demonstrates that if the amenity is also productive, then the sign of the wage gradient is unclear while the rent gradient is positive. The theory is extended to include the housing market and nontraded goods. These extensions require little modification of the conclusion. The empirical work on wages shows that the regional wage differences can be explained largely by these local attributes. With the use of site price data, implicit prices are estimated and quality of life rankings for the cities are computed.
Many applications in economics use multi-sector versions of the growth model. In this paper, we measure the income shares of capital and labor at the sectoral level for the U.S. economy. We also decompose the capital shares into the income shares of land, structures, and equipment. We find that the capital shares differ across sectors. For example, the capital share of agriculture is more than two times that of construction and more than 50% larger than that of the aggregate economy. Moreover, agriculture has by far the largest land share, which mostly explains why it has the largest capital share. Our numbers can directly be used to calibrate standard multi-sector models. Alternatively, if one wants to abstract from differences in sector capital shares, our numbers can be used to establish that this is not crucial for the results. (Copyright: Elsevier)
Differences in house price and income growth rates between 1950 and 2000 across metropolitan areas have led to an ever-widening gap in housing values and incomes between the typical and highest-priced locations. We show that the growing spatial skewness in house prices and incomes are related and can be explained, at least in part, by inelastic supply of land in some attractive locations combined with an increasing number of high-income households nationally. Scarce land leads to a bidding-up of land prices and a sorting of high-income families relatively more into those desirable, unique, low housing construction markets, which we label %u201Csuperstar cities.%u201D Continued growth in the number of high-income families in the U.S. provides support for ever-larger differences in house prices across inelastically supplied locations and income-based spatial sorting. Our empirical work confirms a number of equilibrium relationships implied by the superstar cities framework and shows that it occurs both at the metropolitan area level and at the sub-MSA level, controlling for MSA characteristics.
This paper presents a unified approach to impulse response analysis which can be used for both linear and nonlinear multivariate models. After discussing the advantages and disadvantages of traditional impulse response functions for nonlinear models, we introduce the concept of a generalized impulse response function which, we argue, is applicable to both linear and nonlinear models. We develop measures of shock persistence and asymmetric effects of shocks derived from the generalized impulse response function. We illustrate the use of these measures for a nonlinear bivariate model of US output and the unemployment rate.
Corruption in the public sector erodes tax compliance and leads to higher tax evasion. Moreover, corrupt public officials abuse their public power to extort bribes from the private agents. In both types of interaction with the public sector, the private agents are bound to face uncertainty with respect to their disposable incomes. To analyse effects of this uncertainty, a stochastic dynamic growth model with the public sector is examined. It is shown that deterministic excessive red tape and corruption deteriorate the growth potential through income redistribution and public sector inefficiencies. Most importantly, it is demonstrated that the increase in corruption via higher uncertainty exerts adverse effects on capital accumulation, thus leading to lower growth rates.
A new measure of the local regulatory environment for housing markets: The Wharton Residential Land Use Regulatory Index
- J Gyourko
The price and quantity of residential land in the United States
- M A Davis
Urban decline and durable housing
- E L Glaeser