Dynamic Residential Housing Cycles Analysis

Dynamic Residential Housing Cycles Analysis This paper develops and tests a theoretical model for residential housing market cyclical dynamics. The model employs an interactive supply and demand framework to engender housing price dynamics. Under our set of assumptions, the two equation system is econometrically identified: the first equation, housing demand, relates rent, property values, and capitalization rates with demand fundamentals. The second equation, housing supply, relates housing investment and property values with supply fundamentals. Using the model, we analyze empirically the cyclical dynamics for residential properties in Los Angeles, San Francisco, San Diego and Sacramento for the 1988–2003 time period. The theoretical and econometric design represents improvements and/or modifications of previous studies in at least four ways. First, many of the earlier commercial cyclical analyses have focused on office appraisal and have relied on sparse transactions data, which are likely to be less reliable than the copious amount of residential transactions data. Second, the cyclical volatility and timing of single-family housing is different than that of commercial real estate. Third, by examining different local MSA markets in California, our study distinguishes and isolates national-macro, regional and local market variable effects upon cycles. Finally, utilizing quarterly data (versus annual data) sharpens our ability to focus upon cyclical behaviour. Our empirical analyses suggest that fundamentals, such as employment growth and interest rates are key determinants of the residential real estate cycles. However, in general, local fundamentals tend to have greater cyclical impacts than those of national or regional fundamentals. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Real Estate Finance and Economics Springer Journals

Dynamic Residential Housing Cycles Analysis

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Publisher
Springer Journals
Copyright
Copyright © 2007 by Springer Science+Business Media, LLC
Subject
Economics; Regional/Spatial Science; Financial Services
ISSN
0895-5638
eISSN
1573-045X
D.O.I.
10.1007/s11146-007-9042-x
Publisher site
See Article on Publisher Site

Abstract

This paper develops and tests a theoretical model for residential housing market cyclical dynamics. The model employs an interactive supply and demand framework to engender housing price dynamics. Under our set of assumptions, the two equation system is econometrically identified: the first equation, housing demand, relates rent, property values, and capitalization rates with demand fundamentals. The second equation, housing supply, relates housing investment and property values with supply fundamentals. Using the model, we analyze empirically the cyclical dynamics for residential properties in Los Angeles, San Francisco, San Diego and Sacramento for the 1988–2003 time period. The theoretical and econometric design represents improvements and/or modifications of previous studies in at least four ways. First, many of the earlier commercial cyclical analyses have focused on office appraisal and have relied on sparse transactions data, which are likely to be less reliable than the copious amount of residential transactions data. Second, the cyclical volatility and timing of single-family housing is different than that of commercial real estate. Third, by examining different local MSA markets in California, our study distinguishes and isolates national-macro, regional and local market variable effects upon cycles. Finally, utilizing quarterly data (versus annual data) sharpens our ability to focus upon cyclical behaviour. Our empirical analyses suggest that fundamentals, such as employment growth and interest rates are key determinants of the residential real estate cycles. However, in general, local fundamentals tend to have greater cyclical impacts than those of national or regional fundamentals.

Journal

The Journal of Real Estate Finance and EconomicsSpringer Journals

Published: Jul 27, 2007

References

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