Modeling Spatial Variations in Housing Prices- PDF Download
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Author: Fei Long
Publisher:
ISBN:
Category :
Languages : en
Pages : 274
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Book Description
Author: Fei Long
Publisher:
ISBN:
Category :
Languages : en
Pages : 274
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Book Description
Author: Ayse Can
Publisher:
ISBN:
Category :
Languages : en
Pages : 522
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Book Description
Author: Geoffrey Meen
Publisher: Springer Science & Business Media
ISBN: 1461516730
Category : Business & Economics
Languages : en
Pages : 279
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Book Description
Spatial fixity is one of the characteristics that distinguishes housing from most other goods and services in the economy. In general, housing cannot be moved from one part of the country to another in response to shortages or excesses in particular areas. The modelling of housing markets and the interlinkages between markets at different spatial levels - international, national, regional and urban - are the main themes of this book. A second major theme is disaggregation, not only in terms of space, but also between households. The book argues that aggregate time-series models of housing markets of the type widely used in Britain and also in other countries in the past have become less relevant in a world of increasing income dispersion. Typically, aggregate relationships will break down, except under special conditions. We can no longer assume that traditional location or tenure patterns, for example, will continue in the future. The book has four main components. First, it discusses trends in housing markets both internationally and within nations. Second, the book develops theoretical housing models at each spatial scale, starting with national models, moving down to the regional level and, then, to urban models. Third, the book provides empirical estimates of the models and, finally, the models are used for policy analysis. Analysis ranges over a wide variety of topics, including explanations for differing international house price trends, the causes of housing cycles, the role of credit markets, regional housing market interactions and the role of housing in urban/suburban population drift.
Author: Sue-Ching Jou
Publisher:
ISBN:
Category :
Languages : en
Pages : 388
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Book Description
Author: Greg Smersh
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Land use and location theory suggest that residential land values in an urban area will exhibit distinct spatial patterns of price variation. Moreover, casual observation of housing prices indicates that the level of prices with regard to structural characteristics also varies significantly, although not necessarily in any predictable spatial formation. However, few models have incorporated spatial variables in a manner as to properly capture such price variation. This paper explores alternative methodologies to represent spatial variation in the price of housing. Models are shown to significantly improve estimation relative to an aspatial model and to dramatically reduce spatial dependence in the error terms.
Author: Mauricio Rodriguez
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This research reports results from a competition on modeling spatial and temporal components of house prices. A large, well-documented database was prepared and made available to anyone wishing to join the competition. To prevent data snooping, out-of-sample observations were withheld; they were deposited with one individual who did not enter the competition, but had the responsibility of calculating out-of-sample statistics for results submitted by the others. The competition turned into a cooperative effort, resulting in enhancements to previous methods including: a localized version of Dubin's kriging model, a kriging version of Clapp's local regression model, and a local application of Case's earlier work on dividing a geographic housing market into districts. The results indicate the importance of nearest neighbor transactions for out-of-sample predictions: spatial trend analysis and census tract variables do not perform nearly as well as neighboring residuals.
Author: Kevin Neels
Publisher:
ISBN:
Category : Cost and standard of living
Languages : en
Pages : 26
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Book Description
It has long been recognized that there are large and persistent spatial differences in the cost of living. In spite of this, however, it has remained common for researchers and analysts to assume that prices vary longitudinally but not cross-sectionally, and to ignore spatial variation in the cost of living. In order to know when such an assumption is justifiable and when it is likely to do significant harm to the analysis, we must understand the determinants of spatial variations in the cost of living. This paper discusses the factors which are likely to cause such variations and reports the results of an empirical investigation of their relative strengths. The empirical analysis is based upon the Bureau of Labor Statistics' data on Urban Family Budgets. The model is estimated on a pooled time series/cross-sectional sample constructed from data on thirty-eight metropolitan areas over the period from 1969 to 1977. Much of the cross-sectional variation in the cost of living is explained by differences in climate. Metropolitan areas with cold winters have a higher cost of living than comparable areas located in warmer climates.
Author:
Publisher:
ISBN:
Category : Electronic publications
Languages : en
Pages : 17
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Book Description
This work proposes a non-stationary random field model to describe the spatial variability of housing prices that are affected by a localized externality. The model allows for the effect of the localized externality on house prices to be represented in the mean function and/or the covariance function of the random field. The correlation function of the proposed model is a mixture of an isotropic correlation function and a correlation function that depends on the distances between home sales and the localized externality. The model is t using a Bayesian approach via a Markov chain Monte Carlo algorithm. A dataset of 437 single family home sales during 2001 in the city of Cedar Falls, Iowa, is used to illustrate the model.
Author: Karolien De Bruyne
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
Housing prices vary geographically, even between municipalities. Local differences can be attributed to differences in incomes, demographic effects and real estate characteristics. This paper argues that one should additionally take into account the geographical location of municipalities. In particular, housing prices are affected by distance and travel-time to important economic centers offering jobs and extensive services. Following the economic geography literature, we develop a model showing the impact of geographical barriers on housing prices. We estimate this model on municipality-level housing prices for all 589 Belgian municipalities in 2001. We also differentiate between the two main regions of Belgium (Flanders and Wallonia) as both regions are characterized by political, economic and geographical differences. We distinguish between the attractive forces exercised by both the capital city Brussels and other regional clusters. Our empirical results confirm the expectations. Geographical barriers have significantly negative effects on housing prices. Nevertheless we find important differences between the regions and the means of transport considered.
Author: John M. Clapp
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
This paper is motivated by the limited ability of hedonic price equations to deal with spatial variation in house prices. Host (1999) divides spatial processes into low and high frequency components, inspiring the methods developed here. We further divide Host's low frequency spatial patterns into truly low frequency components, typically modeled parametrically with distance to the CBD or other points of interest, and medium frequency components, modeled here non-parametrically, with local polynomial regressions (LPR). Host, on the other hand, uses LPR for both low and medium frequency variation. LPR gives sufficient flexibility to find substantial spatial variation in house values.We adopt a partially Bayesian approach to modeling high frequency spatial association. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. It allows for prediction at sampled or unsampled locations as well as prediction interval estimates.Out-of-sample mean squared error and related statistics validate the proposed methods. The model is shown to provide insights into the spatial variation of house value.
