The Annals of Applied Statistics

An autoregressive approach to house price modeling

Chaitra H. Nagaraja, Lawrence D. Brown, and Linda H. Zhao

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Abstract

A statistical model for predicting individual house prices and constructing a house price index is proposed utilizing information regarding sale price, time of sale and location (ZIP code). This model is composed of a fixed time effect and a random ZIP (postal) code effect combined with an autoregressive component. The former two components are applied to all home sales, while the latter is applied only to homes sold repeatedly. The time effect can be converted into a house price index. To evaluate the proposed model and the resulting index, single-family home sales for twenty US metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case–Shiller model, which is a repeat sales model, and a conventional mixed effects model. Finally, Los Angeles, CA, is used to illustrate a historical housing market downturn.

Article information

Source
Ann. Appl. Stat., Volume 5, Number 1 (2011), 124-149.

Dates
First available in Project Euclid: 21 March 2011

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1300715185

Digital Object Identifier
doi:10.1214/10-AOAS380

Mathematical Reviews number (MathSciNet)
MR2810392

Zentralblatt MATH identifier
1220.62109

Keywords
Housing index time series repeat sales

Citation

Nagaraja, Chaitra H.; Brown, Lawrence D.; Zhao, Linda H. An autoregressive approach to house price modeling. Ann. Appl. Stat. 5 (2011), no. 1, 124--149. doi:10.1214/10-AOAS380. https://projecteuclid.org/euclid.aoas/1300715185


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Supplemental materials

  • Supplement to “An autoregressive approach to house price modeling”. This supplement contains extra analysis on a variety of topics related to the paper from examining the convergence of the coordinate ascent algorithm, or applying alternate loss functions, to studying the impact of each feature included in the autoregressive (AR) model.
    Digital Object Identifier: doi:10.1214/10-AOAS380SUPP
    Supplemental PDF (594 KB)

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