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1980, Journal of Urban Economics
Spatial variations in income have not been adequately accounted for in urban density regressions. Estimating equations incorporating household income are derived in a monocentric urban model. The technique used also yields an estimate of the income elasticity of demand for housing, found to be less than one.
Oxford Bulletin of Economics and Statistics, 1998
Journal of Urban Economics, 1992
Journal of Economic Theory 9, 223-237, 1974) asserted that an increase in incomes lowers central densities raising suburban densities in closed monocentric cities with identical resident incomes and tastes. We show that with identical Cobb-Douglas tastes and identical incomes, a sufficient condition for densities to fall everywhere when incomes rise is that the land rent bill of the innermost resident be larger than the commuting bill of the outermost resident. Estimating this model using data in E. S. Mills ("Studies in the Structure of the Urban Economy," Johns Hopkins Press, Baltimore/London, 1972), we show that densities are predicted to fall everywhere in Baltimore, Denver, Milwaukee, Philadelphia, Rochester, and Toledo when income is increased by 10% circa 1960. But, when we disaggregate the model using the 1960 census income distribution (keeping tastes identical), a 10% increase in incomes rotates densities around distances which vary from 3.3 miles in Toledo to 9.8 miles in Philadelphia. In addition to empirically vindicating Wheaton's assertion, the disaggregated model gives approximately negative exponential densities consistent with spatial equilibrium, and without resorting to R. F. Muth's ("Cities and Housing", University of Chicago Press, 1969) assumption of a unitary compensated price elasticity of demand. 0 1992 Academic Press. Inc. 'We thank Jan Brueckner for reading this paper and for helpful comments, and Edwin Mills and an anonymous reviewer for suggestions.
NBER Books, 1975
2013
As metropolitan governments explore density-promoting "smart growth" policies, finer analysis is needed to quantify the impact of such changes on households' transportation and housing costs. Existing research suggests that households in urban areas face a trade-off between living in areas with higher housing costs and lower transportation costs or the reverse, but does not explore how density changes explicitly impact this balance. This paper uses the 2000 Census Public Use Micro Sample (PUMS) data from twenty-three of the nation's most densely populated states to identify the impact of increased population density on household rents, housing unit values and monthly mortgage payments. The project additionally explores
Computational Science and Its Applications - ICCSA 2016, 2016
Urban growth processes are notoriously complex, depending on vastly different demographic, socio-cultural and economic factors. The analysis is even more complex in the metropolitan areas, since they are the result of ancient agglomeration processes in a phase of intensive development of settlement and, more recently, of the formation of urban polycentrism. Investigation requires collection, analysis and processing of useful information at homogeneous terri‐ torial units, based on already consolidated models or through new validating protocols. The present paper analyzes urban growth based on a micro-scale approach, identifying homogeneous local districts for localization features that can affect real estate market value for residential use. These features include the location of the housing unit compared with the city center, the level of infrastructure, the presence of community facilities and shops, but also the external environment quality in terms of availability of green public and air pollution degree. Geographic Information System applications are used to process the available dataset to identify, at first, the demographic evolution of Naples as a functional urban region according to the life cycle model proposed by Van den Berg and, then, the real estate dynamics of the metropolitan in the light of income flows which each asset is capable of producing. Understanding the spatio-temporal evolution of real estate property values can be useful to explain the intimate mechanism of urban growth at the metro‐ politan scale.
Policy Research Working Papers, 2014
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at .
Real Estate Economics, 2005
This article establishes a linkage between decadal changes in suburban population and the supply of suburban dwelling units. It then estimates an econometric supply-and-demand model for 317 U.S. suburban areas for the 1970s, 1980s and 1990s using the State of the Cities database. Suburban supply is more elastic than central city supply, with suburban estimates between +1.26 and +1.42. However, separate estimates by geographic region lead to supply elasticities of +0.89 for the northeastern quadrant of the United States and +1.86 for the remainder of the United States. This article addresses issues of population change and housing supply in U.S. suburbs. Central cities often have only limited opportunities for new construction, while surrounding suburbs "beyond Eight Mile Road" may have considerable vacant land to accommodate new employers and new residents. 1 This generalization, of course, oversimplifies. New Rochelle, NY; Evanston, IL; Brookline, MA; Royal Oak, MI; and Lakewood, OH, for example, were developed 100 or more years go. Many suburbs (Puentes and Orfield 2002) are fully built up, many have stopped growing or have experienced population losses, and some have problems of blight or poverty similar to central cities. This article establishes a linkage between decadal changes in suburban population and housing supply, differentiating between central cities and inner and outer suburban rings. It then estimates an econometric supply-and-demand model for 317 U.S. suburban areas for the 1970s, 1980s and 1990s using the State of the Cities database. With almost all suburban areas characterized by increasing housing stock and in general more buildable land than the central cities, one would expect suburban supply price elasticities to exceed those of central cities. Using a similar model, Goodman (2004) estimated dwelling unit price elasticities between +0.03 and
Geographical Analysis, 2010
Housing Price Gradients: The Intersection of Space and Built Form A hedonic housing price model, implemented for the Dallas region, reveals a housing market structured around multiple nodes, some of which give rise to positive and others to negative externalities. The utility / disutility derived from relative location is capitalized into the size and quality of the housing stock and the nature of neighborhood amenities. The result i s a convergence of space and built f m. Even the most cursory examination of housing price variations in modem American metropolitan regions reveals a complexity of spatial patterns unanticipated and unexplained by the monocentric model of Alonso (19641, Muth (19691, and Mills (1967). In this paper we examine these patterns as they have been represented in the Dallas housing market since 1985. In particular, we explore relationships between relative location, housing characteristics, and neighborhood amenities in the determination of housing prices. The literature on hedonic price models of the housing market calls on the Lancastrian concept of housing as a bundle of services, the implicit prices of which can be estimated by regression of sales prices on an array of these components. Typically, the housing services are grouped into structural characteristics (size, age, quality), neighborhood amenities (characteristics of the neighborhood housing stock and demographics, school quality), and location (most commonly distance to the central business district (CBD), as suggested by the monocentric model). Recently, more attention has been paid to the locational aspects of housing markets in modem, sprawling cities such as Los Angeles (Heikkila et al. 1989) and Dallas (Waddell, Berry, and Hoch 1993) by incorporating accessibility to suburban employment centers, expressways, and other potential nodes and axes of influence. As these models are extended to include more spatial variables, the likelihood of
Journal of economic and social measurement
Accurate description of the distribution of housing units within sub-County geographies is an important component of small-area population estimation. This paper pilots the use of the Pearl-Reed logistic model to predict housing unit growth in urban Census tracts in Bernalillo County, New Mexico for 2007. The model is based upon 1990 to 2000 growth rates, constrained with respect to a priori estimates of an upper-limit of housing units that could potentially be built within a tract based on its land area. In spite of the simplistic nature of this model, it is found to perform quite well. Further development based on incorporation of additional economic, demographic, and sociologic data would likely improve the model substantially; however, in this study the model out-performed standard trend extrapolation procedures for the study area and displayed error measures comparable to those reported in the literature for extrapolation methods in general.
Housing Policy Debate, 2020
Findings from a study using the Panel Survey of Income Dynamics (PSID) and detailed urban environment and transit data support the location affordability hypothesis. Households in location-efficient places spent significantly less on household transportation, enough to offset high housing costs. Walkable blocks and good transit especially contribute to these savings. But households with very low incomes (below 35% AMI) do not see significant enough savings. Authors recommend investments in transit, sidewalks, and economic development in disinvested areas; the preservation and creation of affordable housing of all types and tenures; and more supports for households with very low incomes. For decades, researchers have explored how location efficiency (LE) affects housing affordability, including incorporating transportation costs into a holistic housing affordability measure known as location affordability. Others have argued that estimated transportation savings from LE may be overstated because of limits in data and methods. Smart and Klein's 2018 article in Housing Policy Debate analyzed the PSID and found "no evidence to support the location affordability hypothesis." Considering their study's policy implications, as well as its methodological limitations, we tested the PSID data at a smaller geography using more detailed household and urban form variables, per the LE literature. With this approach, we find statistically significant and meaningful transportation cost differences that are enough to offset higher housing prices for several income groups. However, the transportation savings for households in the lowest-income group in urban areas do not offset high housing costs. Because location-affordable places are in short supply, and the extreme shortage of affordable housing, both housing and transportation investments are needed to support households with low and moderate incomes. Expanding location affordability regionally will also help to address climate change and expand access to job opportunities, goods, services, and other amenities.
Journal of Urban Economics, 1986
Regional Science and Urban Economics, 1977
The application of monocentric models of residential location to the analysi'; of metropolitan areas with more than one center of economic activity, produces a disto~'tt::d view of the spatiai distribution of urban variables such as land values, housing prices, etc. This distortion result~ from the fact that monocentric models tend to underestimate the values of these variables in areas lying between the centers, and yield wider residential areas toward the outskirts of the city. In this paper, a mod,:l of household location is developed, which attempts to correct, this distortion by simultaneously considering the urban centers during the rcsideotial location process.
Regional Science and Urban Economics, 1985
Many economists criticize the concept of the composite commodity 'of housing that forms the basis of modern urban economics. As a result, much empirical work has been produced that attempts to estimate the household demand for housing and locational characteristics. The purpose of this paper is to take stock of the literature. The theoretical foundations of the literature and the econometric procedures employed are analyzed and critiqued. In addition, the empirical results are examined in order to identify any patterns that exist. The principal conclusion of this survey is that the theoretical basis is sound, but the econometric applications leave much to be desired. One consequence is that the literature has produced few empirical regularities. Another is that more studies using better estimation procedures and better data are needed before it can be safely argued that the composite commodity concept is replaced by the characteristics approach. These include Jan Brueckner, Jan Ondrich, Doug Diamond and Serena Ng. Of course, all errors and opinions are our own.
Geographical Analysis, 2010
Another Challenge to the Monocentric Model Extension of hedonic modeling strategy to the case of apartment rents in the Dallas region reveals the influence of a complex network of activity centers and highway axes. Nodes other than the CBD exert greater influence on rents than the CBD and amenity variables and externalities have both positive and negative eflects. These findings demand reevaluation of the place nonnally assigned to higher-density rental housing in urban models. There has been little research in urban economics or urban geography on the spatial variation of apartment rents. Thinking has been dominated by the monocentric model that predicts that the rent of land devoted to apartment use will be highest closest to the urban center and will decline with distance from the central business district (CBD) less rapidly than the rents of CBD (commercial) land uses but more rapidly than single-family rents, creating an inner high-density ring of residential land use around the CBD (Mills and Hamilton 1989).' This model is belied by the empirical results presented in this paper. In the multinodal Dallas-Fort Worth metroplex, apartment rents are higher around other nodes than the CBD, and there is a richness of spatial variation related not simply to the multiplicity of nodes, but also to the networks of expressways and other road arteries, as well as to other land uses within the neighborhood or adjacent to the property in question. It seems plausible that these patterns hold for the multinodal metropolis, generally, implying the need for more complex formulations of spatial patterns than the monocentric model. In what follows, we describe data and methods used to identify empirical spatial patterns in the Dallas area, discuss our principal findings, and conclude with likely implications for improved model-building, based on those findings. An appendix presents detailed documentation of the key empirical results. 'Mills and Hamilton develop the monocentric model in detail, and make it a central component of
Journal of Housing Economics, 2005
Many older American cities lost population during the last three decades of the twentieth century, but while cities such as Boston or New York saw numbers of dwelling units remain stable or even increase, others such as Buffalo, St. Louis, Cleveland, Detroit, and Pittsburgh lost large fractions of their dwelling units. This study decomposes decadal population changes from 1970 through 2000 for 351 US cities into household size, housing unit, and occupancy rate effects and finds substantial stock declines (as high as 50%) in many cities. It then develops a supply and demand model to model central city housing unit supply elasticities, with special emphasis on ''kinked supply''-inelastic in the negative direction and elastic in the positive directions. Supply elasticities for housing unit decreases were between +0.03 and +0.13. For housing unit increases the elasticities were between +1.05 and +1.08.
Regional Science and Urban Economics, 2015
Previous empirical investigations provide evidence of substantial regional variation in the supply elasticity of housing, and show that the elasticity and its variation across cities within the U.S. are significantly influenced by regulatory supply constraints, city level population, population density, and geographic constraints. This paper studies empirically if these findings apply to a country that is notably different from the U.S. with respect to its population density, typical city size, geographic and cultural coherence, and regulatory constraints, i.e., Finland. Based on data for the period 1987-2011, our findings are largely in line with those reported for the U.S. The results support the theoretical models indicating that the supply elasticity is largely a local phenomenon, i.e., dependent mainly on city specific factors rather than the abundance of undeveloped land at the country level. The long-term supply elasticity substantially varies across Finnish cities. The city size, zoning policies, and geographic constraints are found to be the most important factors causing regional elasticity differences, accounting for some 80% of the elasticity variation.
Environment and Planning B: Planning and Design, 2007
Introduction The hedonic housing price model is a powerful econometric tool for capturing important determinants of prices/housing values regarding structural and locational (neighborhood) attributes, and has been widely used in housing and urban studies. Serving as a``joint-envelope of a family of value equations (consumers' preferences) and another family of offer functions (suppliers' technologies)'' (Rosen, 1974, page 44), the hedonic model establishes a formal relationship between housing values/prices and a set of housing attributes (the quantity and qualities embodied in housing). Usually, housing attributes contain not only structural attributes such as floor size, but also locational or neighborhood conditions, such as proximity to certain public facilities. The model is appealing in that the implicit price of various housing attributes can be estimated from the model. Traditionally, the regression is calibrated through the ordinary least squares (OLS) estimator, under the general assumption of independent observation. However, despite the mature OLS technology and its wide application in examining the relationships between housing prices and attributes (for a review see Can, 1992), the full potential of the hedonic model remains to be exploited (Ekeland et al, 2004), and locational attributes in particular have drawn inadequate attention (Orford, 2002). During the late 1980s and early 1990s, largely due to the advancement in spatial statistics and spatial econometrics (
RePEc: Research Papers in Economics, 2000
In this paper, we estimate a model of housing demand with neighborhood effects. We exploit special features of the National sample of the American Housing Survey and properties of housing markets that allow us to create "natural" instruments and therefore identify the impact of social interactions. We find evidence of both endogenous and contextual neighborhood effects. We report two alternative sets of estimates for neighborhood effects that differ in terms of the instruments we use for estimating the model. When the endogenous neighborhood effect is large the respective contextual effects are weak, and vice versa. The elasticity of housing demand with respect to the mean of the neighbors' housing demands (the endogenous effect) ranges from 0.19 to 0.66 and is generally very significant. The contextual effects are also very significant. A key such effect, the elasticity with respect to the mean of neighbors' permanent incomes ranges from 0.17 to 0.54. JEL Classification Codes: R21, C31.
Density in urban areas is not a simple concept. It can be defined in a variety of ways. This paper presents alternative measures of urban density and compares how they perform across a set of 59 large urban areas in the United States in 2010. Population and housing unit densities show similar patterns, though with differences for some areas with extreme population-housing unit ratios. The housing unit densities do perform better in the estimation of the negative exponential model of density decline. The conventional measure of total population divided by the total land area of an urban area differs from various measures of the density of residential areas. The absence of data precludes the calculation of residential densities for large numbers of urban areas, but using block data and excluding blocks that are completely or largely nonresidential makes possible alternative measures coming closer to residential density. When used to estimate the negative exponential model, these measures produce higher estimates of the parameters and a significantly greater goodness-of-fit. Weighted density calculated as the mean of the densities of small areas weighted by their population is another, completely different measure. These are higher than convention densities, with the differences being larger for those urban areas with greater internal variation in density, such as areas with high density cores and low density peripheries.
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