Estimation employing a priori information within mass appraisal and hedonic pricing models
✍ Scribed by R. Kelly Pace; Otis W. Gilley
- Publisher
- Springer US
- Year
- 1990
- Tongue
- English
- Weight
- 1005 KB
- Volume
- 3
- Category
- Article
- ISSN
- 0895-5638
No coin nor oath required. For personal study only.
✦ Synopsis
Both statistical appraisal and hedonic pricing models decompose houses into a set of individual characteristics. Regression estimates yield the contribution of each characteristic to total value. Unfortunately, straightforward application of OLS may produce untenable results such as implausible coefficient magnitudes or incorrect signs. Often the suspected cause is multicoUinearity. This article examines the effect on estimation efficiency of differing levels of multicollinearity, R 2, and a priori information in the form of sub-market cost data, by comparing inequality restricted least squares (IRLS) with OLS in a series of Monte Carlo experiments. The IRLS procedure investigated here hybridizes the statistical market approach implemented by OLS, and the more traditional cost approach. The experiments show dramatic gains in estimation efficiency from exploiting a priori information through IRLS.