El Paso Housing Sector Econometric Forecast Accuracy

By Fullerton, Thomas M., Jr.; Kelley, Brian W. | Journal of Agricultural and Applied Economics, April 2008 | Go to article overview
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El Paso Housing Sector Econometric Forecast Accuracy


Fullerton, Thomas M., Jr., Kelley, Brian W., Journal of Agricultural and Applied Economics


There is comparatively little empirical evidence regarding the accuracy of regional housing sector forecasts. Much of the recent analysis conducted for this topic is developed for housing starts and indicates a relatively poor track record. This study examines residential real estate forecasts previously published for El Paso, TX using a structural econometric model. Model coverage is much broader than just starts. Similar to earlier studies, the previously published econometric predictions frequently do not fare very well against the selected random walk benchmarks utilized for the various series under consideration.

Key Words: applied econometrics, metropolitan housing sector forecasts

JEL Classifications: C53, R15, R31

Regional housing sector forecasts are widely used to shape public policy and business decisions (West 2003a). They are often reported in the media as business cycle indicators and can serve to inform public opinion about the current state of the economy. Despite their widespread usage, relatively little research has examined the accuracy of housing sector forecasts. Time constraints plus contractual obligations provide commercial economists with little incentive to perform such tests. Lack of access to complete data sets makes it difficult for academicians to undertake research in this area. This study takes advantage of such a data set to perform accuracy analyses for housing sector forecasts compiled over time for a relatively large metropolitan market in Texas.

Data utilized consist of residential real estate forecasts published by the University of Texas at El Paso Border Region Modeling Project between 1998 and 2003. The housing sector of the model includes variables such as starts, stocks, prices, and affordability for the El Paso County Metropolitan Statistical Area (MSA). El Paso is the sixth largest MSA in Texas and is located directly across the border from Ciudad Juárez, the largest city in the state of Chihuahua in Mexico. The model is used to generate econometric forecasts of El Paso and Ciudad Juárez, as well as Chihuahua City and Las Cruces. Chihuahua City is the capital and second largest city in the state of Chihuahua. Las Cruces is the second largest MSA in New Mexico. Housing equations are included in the model only for El Paso (Fullerton 2001). Macroeconomic data for the United States and Mexico are used as explanatory variables in many of the equations and obtained from Global Insight (Alemán; Behravesh, Hodge, and Latta).

The forecasts are ex ante in the sense that all of the model predictions published each year are for periods beyond those used in parameter estimation. As such, they satisfy the evaluation criteria established in several earlier studies for realistic model assessment (Christ; Granger; Howrey, Klein, and McCarthy). Along those same lines, preliminary estimates for El Paso housing data are not available during the year in progress in the manner that such data are for unemployment rates or transportation aggregates. Although the housing sector estimation results are generally good, it is well known that good in-sample fits do not guarantee reliable out-of-sample simulation performance (Learner; McCloskey and Ziliak). Given the important role that residential real estate plays in local economic performance, assessment of housing model forecasting performance merits additional attention (Reback; Smith and Tesarek).

Although recognition that real estate forecast assessment is useful, how to carry out such an exercise is not immediately obvious (McNees 1978; West 2003a). To say that forecast errors are large or small is meaningless without a frame of reference (McNees 1992). The required accuracy of a forecast will, in large part, depend on the way in which the forecast is used. Preferably, a standard can be generated from a long history of previous results for a variety of statistical and econometric models that forecast the same types of data, or for one model type forecasting across a large number of regional markets.

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