When Zillow introduced the Zestimate and Zillow Home Value Index in 2006, one of the first things consumers asked for next was an estimate of where home values were going to go in the future. While we periodically produced a national forecast of home values for the purposes of analyzing national economic trends, we did not publish a regular national forecast nor forecasts for the various metropolitan regions.
With the release of our Q4 data this week, we are happy to correct this state of affairs by announcing the Zillow Home Value Forecast (ZHVF). The ZHVF forecasts the change in the Zillow Home Value Index (ZHVI) over the next twelve months for the 25 top metros and the nation as a whole. The ZHVI itself is a time series tracking the monthly median home value in a particular geographical region, and the methodology behind the index is described in more detail in this research brief.
Below, we will detail the basic approaches we’ve used to construct the ZHVF and its historical performance.
A quick primer on characteristics of home value time series
Since the ZHVI is a time series of home values, it inherits many of the characteristics found in other home price indexes (Canarella, Miller, Pollard, Gupta). These characteristics include:
To address these characteristics, the Zillow Research team designed a forecasting approach with the following considerations:
Multiple model approaches
The Zillow Home Value Forecast employs multiple models, each one belonging to either a univariate time series or multivariate economic leading indicator family of models.
Time series models
Time series models utilize the past history of a time series to predict the future evolution of that time series. Implementing a time series model may also include transforming the series (e.g. Box-Cox), differencing or calculating returns on the series, as well as time series decomposition into trend, seasonal, and irregular components. All of these techniques were explored as part of the model research process.
A number of alternative time series models implemented and evaluated including ARIMA models (Box-Jenkins), structural time series models (Harvey, Shephard, Durbin, Koopman), and exponential smoothing models (Holt, Winters, Gardner, McKinzie, Hyndman). Ultimately, the Zillow Research team chose a damped-trend exponential smoothing model as one of the core models for the ZHVF.
Economic leading indicator models
As opposed to a univariate time series model which relies solely on the history of the time series being modeled, an economic leading indicator model attempts to relate a number of economic variables to the time series being modeled. In this context, we developed an economic leading indicator model which utilizes macro-economic variables to forecast future home value changes. Some of the explanatory variables in this model include home sales, monthly supply of inventory, income growth, rent levels and unemployment. The economic leading indicator model is the other core model for the ZHVF.
Synthesizing various models outputs
The ZHVF is a combination of forecasts (Bates, Clemen, Timmerman) based on input from both the time series models and the economic leading indicator models. The weight of each model’s input into the final forecast is determined dynamically as a result of the model’s recent forecast accuracy.
Historical forecast accuracy
The ZHVF forecasts the change in the ZHVI over the next twelve months. To assess the 12-month ahead out-of-sample predictive accuracy of the ZHVF, the Zillow Research team implemented a technique called time series cross validation (Hyndman). Using this procedure, we forecast historically using only data known at the start of a simulated forecast period. In this process, we simulate a forecast made for a future period and then compare this forecast with the actual results over that same period of time.
The following table summarizes the 12-month ahead cross validation forecast accuracy for all regions combined and for just the United States time series itself:
Not surprisingly, one can see that forecast accuracy over the most extreme periods of the home value boom and bust is lower than during the recent period of time when home value trends have been more stable (albeit negative).
Future research and development
Zillow is committed to ongoing research and continuous improvement in ZVHI forecasting accuracy. Some of the research activities planned for the near term include the following:
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