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The Value of Housing Characteristics: A Meta Analysis

The Value of Housing Characteristics: A Meta Analysis This paper provides a meta regression analysis of the nine housing characteristics that are appear most often in hedonic pricing models for single-family housing: square footage, lot size, age, bedrooms, bathrooms, garage, swimming pool, fireplace, and air conditioning. Meta regression analysis is useful for comparing the estimated regression coefficients from different studies. The goal in this study is to determine if the estimated coefficients vary by geographical location, time, type of data, and model specification. The results show that the estimated coefficients for some characteristics vary significantly by geographical location. These include square footage, lot size, age, bathrooms, swimming pool, and air conditioning. Controlling for time shows that the effects of these housing characteristics on house price have not changed over time. Controlling for type of data produces differences in coefficients for bathrooms. Controlling for wealth as measured by median household income has no significant impact on the coefficients for the housing characteristics. If the study controlled for square footage, the coefficients for lot size decrease. Controlling for the size of the hedonic model affects the coefficient for square footage. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Real Estate Finance and Economics Springer Journals

The Value of Housing Characteristics: A Meta Analysis

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References (78)

Publisher
Springer Journals
Copyright
Copyright © 2006 by Springer Science + Business Media, LLC
Subject
Economics; Regional/Spatial Science; Financial Services
ISSN
0895-5638
eISSN
1573-045X
DOI
10.1007/s11146-006-9983-5
Publisher site
See Article on Publisher Site

Abstract

This paper provides a meta regression analysis of the nine housing characteristics that are appear most often in hedonic pricing models for single-family housing: square footage, lot size, age, bedrooms, bathrooms, garage, swimming pool, fireplace, and air conditioning. Meta regression analysis is useful for comparing the estimated regression coefficients from different studies. The goal in this study is to determine if the estimated coefficients vary by geographical location, time, type of data, and model specification. The results show that the estimated coefficients for some characteristics vary significantly by geographical location. These include square footage, lot size, age, bathrooms, swimming pool, and air conditioning. Controlling for time shows that the effects of these housing characteristics on house price have not changed over time. Controlling for type of data produces differences in coefficients for bathrooms. Controlling for wealth as measured by median household income has no significant impact on the coefficients for the housing characteristics. If the study controlled for square footage, the coefficients for lot size decrease. Controlling for the size of the hedonic model affects the coefficient for square footage.

Journal

The Journal of Real Estate Finance and Economics – Springer Journals

Published: Nov 13, 2006

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