Inland Empire Residential Home Prices Flat for the Next Two Years

Mak Kaboudan Analysis, Archive, Housing, Methodology

Inland Empire Residential Home Prices Flat for the Next Two Years


The inflated home prices that characterized the US housing market by late 2006 were followed by high rates of mortgage-loan defaults that ultimately led to seriously depressed home prices. Almost everyone who lived the housing bubble and experienced the negative wealth effects the bubble had since 2007 is now waiting and hoping to see a rebound in real estate investments that could take us out of the noticeably depressed economic conditions. This report sheds some light on what to expect throughout 2012 for home prices in nine cities located in three different counties in Southern California. To generate the forecasts, each city was first divided into blocks identified according to the Assessor’s Parcel Numbers. Forecasts of each block’s average prices were then obtained using genetic programming (an artificial intelligence method). Forecasts of home prices belonging to city-blocks with similar prices were then aggregated to produce four city-clusters of hierarchical price-ranges. These forecasts suggest that prices will at best remain sluggish for many geographical locations. Expectations of a housing market rebound are in the more distant future and should not occur without the unemployment rate decreasing to sustainably lower levels (at least below 6%) first and only if these rates stay there for a while. It will take at least several years of persistent economic health before home prices start to rebound.

Forecasted residential home prices for the month of March 2012 in nine selected Southern California cities


The current economic conditions in the Inland Empire have reached levels that concern residents and policy makers alike. While economic conditions may be marginally improving throughout most of California, they are not improving in the two Inland Empire Counties of Riverside and San Bernardino yet. The unemployment rate in the city of San Bernardino is currently above 18% while in the cities of Riverside and Ontario the rates are around 15%. The current depressed economic conditions are a consequence of the 2006-2008 housing bubble. Starting 2002, economic growth was fueled by a strong sellers’ housing market along with robust housing construction. When a strong buyers’ market crept in by late 2006, it forced new construction permits early in 2007 to practically reach a halt. Questionable lending and securitization practices, falling home prices and, the decline in home construction activities quickly created a financial crisis and depressed economic conditions. In the four years that followed, the unemployment rate steadily increased. The higher unemployment rate levels imposed more pressure on the already ailing and overstocked housing market.

The housing market and economic health are interdependent. Healthy economic environments fuel housing market growth while strong housing market conditions fuel economic growth. Conversely, weak economic environments create stagnant housing markets while weak housing market conditions erode economic prosperity. [The housing market is considered strong or healthy when homes for sale are on the market an average of 60 days or less, the foreclosure rate is below 1% of outstanding loans, percentage changes in home prices are just above the inflation rate, there is six months or less of supply of homes for sale, and the number of new home construction permits holds steady.] When tracing this interdependency, accounting for time is critical. Strong housing markets transform rather fast into healthy economic environments. But healthy economic environments transform very slowly into strong housing markets. Conversely, weak housing market conditions swiftly transforms into ailing economic environments while the negative impact of weak economic conditions on the housing market are relatively less rapid. In assessing current 2011 conditions, unemployment rates are unacceptably high and the housing markets are wretchedly depressed. With poor economic conditions, the housing market cannot bootstrap itself. Therefore, economic conditions must improve first before the housing market rebounds. The improvement in economic conditions will then be followed by slow reaction in the housing market since banks need to reestablish confidence in rebounding markets before extending new real estate loans. New homes construction will be slow because of the existing huge inventory of homes for sale. This means that even if economic conditions improved soon, it will take potential home buyers, home builders, and lending institutions two to three years before reacting to any improved and sustained economic conditions.

The purpose of this report is to present predictions of residential home prices mainly in the Inland Empire. The background information presented above exposes expectations about residential home prices. The forecasts presented here are for six selected cities in Riverside County and San Bernardino County (the two Inland Empire Counties) and three cities in their bordering Orange County for comparison.


All forecasts produced and reported here are obtained using genetic programming or GP. GP is a computerized optimization random search technique that mimics regression modeling. The computerized algorithm selects the best variables from a larger set to explain prior changes in home prices and produce a forecasting model.

GP’s initial task is to identify the most pertinent variables from the furnished set. To identify those variables, GP produces a minimum of 100 equations for each location first. The regression-like models are compared and the ones with the least mean square regression errors are carefully analyzed to identify the most pertinent variables that repeatedly appeared in those regression equations. Using this method, it was possible to identify nineteen independent variables initially that explained variations in all neighborhoods monthly average prices. The nineteen variables include monthly averages of each neighborhood’s home-characteristics namely: (a) square footage of homes sold, (b) number of bedrooms, (c) number of bathrooms, (d) ages of those homes, and (e) dummy variables to account for swimming pools. The explanatory variables also include six lagged mortgage rates (specifically seven to twelve monthly lags) and seven lagged unemployment rates (specifically six to twelve monthly lags). Mortgage rates (which are identical for all locations) are a proxy variable for cost. The unemployment rates variable (whose data differed from one city to the other but was identical for all neighborhoods within a given city) is a proxy variable for income. Pertinent variables identified then became the input to produce a second round of regression models. The following steps were followed to produce the models to forecast each city’s neighborhood prices:

  • (a) Neighborhoods were defined using the Assessor’s Parcel Numbers or APNs. APNs are property identification numbers (much like social security numbers). APNs of adjacent properties typically only differ in a few rightmost digits. To define a “neighborhood”, APN’s of sold properties are truncated to the three leftmost digits. Properties with identical three leftmost digits then make up a “neighborhood”. Average prices of homes sold for each month in each neighborhood (after eliminating outliers) are the values of the dependent variable to fit and forecast.
  • (b) GP then evolved a different model to fit each neighborhood’s average prices over the last eighteen months (January 2010 through June 2011, inclusive).
  • (c) Models considered statistically desirable were then used to predict average neighborhood prices through December of 2012. (Although GP used the same commonly identified explanatory variables it produced different specifications for the different neighborhoods. The resulting models thus did not have the same identical number of variables nor the same variables.)
  • (d) Because each city had a different number of neighborhoods, four equally spaced ascending price ranges were used to define four city clusters. Accordingly, each city was represented by four clusters identified as C1 for homes sold in neighborhoods at the lowest range of prices, C2 for the second lowest, C3 for the third, and C4 for the cluster with the highest range of prices. Quarterly home-price forecasts through December 2012 for each city with its four clusters are provided below.

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Inland Empire Residential Home-Price Forecasts