An autoregressive approach to house price modeling

TL;DR

This paper introduces an autoregressive statistical model for predicting house prices and constructing a house price index, leveraging sale details, timing, and location, with improved predictive accuracy over existing models.

Contribution

It presents a novel autoregressive approach combining fixed time effects and random ZIP code effects for house price modeling and index construction.

Findings

Model outperforms S&P/Case-Shiller in predictive accuracy

Effective in capturing housing market downturns

Applicable to multiple US metropolitan areas

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.