Empresa Ecuacorriente S.A (ECSA)

The hedonic price model was chosen to estimate the effect of foreclosures on existing housing prices. A semi-logarithmic model may help reduce the problems of heteroskedasticity (Halverson and Pollakowski, 1980; Malpezzi, 2002; Malpezzi, Ozanne, and Thibodeau, 1980). Thus, the equation is estimated with the selling price as the dependent variable in semi-log form followed by five vectors for this research:

lnP = β0 +Σβ1H + Σβ2M + Σβ3S + Σβ4SF +Σβ5CF + ε [4.2]

Where the term H denotes housing physical characteristics, M indicates quarter dummies controlling for market price trends, S stands for selling characteristics associated with foreclosure status on the property, SF denotes neighboring foreclosure filings for single family homes, and CF is neighboring foreclosure filings for condos, respectively.

Extensive previous research regarding housing values indicates a positive relationship typically exists between property characteristics and the dependent variables. Housing physical variables include total acreage of lot size (LOT_SIZE), square footage of the living area (LIVING_AREA), building age (AGE), garage (GARAGE), swimming pool (POOL), and stories (STORY) of each home type.

The building age (AGE) variable represents a slightly more complex situation. Typically, the age of housing stock is viewed as an indication of deterioration or

obsolescence thereby resulting in lower property values. However, there are older homes in some neighborhoods whose values have remained competitive with newer homes. Furthermore, Goodman and Thibodaeu (1995) found that there was actually a curvilinear pattern between age and housing valuation, meaning that not controlling for the nonlinear effects of age causes heteroskedasticity in the model’s residuals. Thus, a quadratic form was allowed to control curvilinear pattern.

Quarter dummy variables are included to account for whether the property was sold in the second, third, or fourth quarter, with the first quarter being the omitted dummy variable. There are no sign expectations in any of the time-related variables because both supply and demand for housing will change during each period.

A vector for selling characteristics of properties, which is related to foreclosure status, measures the marginal impact of renter occupancy status and cash transaction on selling prices. These two variables, depending on foreclosure status, tend to be associated with the price discount in the transaction event.

The final two terms related to foreclosure variables will account for the potential marginal impact of neighboring foreclosures by counting foreclosures within specific distances.

Nonlinear effects test for nearby foreclosures

The previous studies for foreclosure effects were mainly based on a linear model of the relationship between foreclosure growth and housing price change. One possible concern is that the impact of foreclosures on prices may reflect nonlinear effects as

discussed in the section of hypotheses and conceptual models (that is, a rise in foreclosures at a specific distance has a diminishing negative effect on nearby home prices as the rise in foreclosures increases) Thus, equation [4.3] was extended to allow for nonlinear effects in quadratic form:

lnP = β0 +Σβ1H + Σβ2Q + Σβ3S + Σβ4SF + Σβ5SF2 +Σβ6CF + Σβ7CF2+ ε, [4.3]

This specification also allows the marginal price impact to vary with the frequency of existing foreclosures in an area. It is expected that few foreclosures will have a small price-depressing impact in the neighborhoods. But, as foreclosures begin to accumulate during housing bust cycles, the cumulative price-depressing impact will be larger in areas with a high density of foreclosures.

Concept measurement of neighboring foreclosures

One of significant challenges in this study is how to isolate and measure the impact of neighboring foreclosures on home sale prices. Essentially, this study defines "nearby” or “neighboring" in three alternative ways (three rings) in order to measure fixed effects for these micro-neighborhood level or smaller scales. In the presentation of the models, these are referred to as Ring 1 (0 to 500 feet), Ring 2 (501 to 1000 feet), and Ring 3 (1001 to 1500 feet).

In this fashion, the impact can be estimated over different spatial scales since the effect can vary with distance. Thus, this approach would allow for the notion of distance

decay of the impact, where the effect of the externality decreases as distance increases. This approach avoids having to choose an arbitrary distance within which the externality (foreclosure) is hypothesized to have an impact, and beyond which there is no impact expected. This procedure also provides a better way to capture the impact of spatial heterogeneity on house prices. Note that the measured effects of the three concentric rings (maximum distance = 1500 feet) chosen are assumed to impact all properties equally within each concentric circle (see Figure 4.8).

Figure 4.8. Concept Measurements of Neighboring Foreclosure Effects on Existing Home Sale Prices.

This study applies to the model of spatial fixed effects. A review of the literature (see tables on pages 46-47 [Table 2.2]) does suggest different size rings be included in hedonic regressions as seen in Figure 4.8. This measurement was designed in anticipation that there would be observable patterns of change in property values with closer proximity to foreclosures. Improvements in the field of Geographic Information Systems (GIS) support efficient and accurate measurement.

In order to minimize the problem of reverse causation, the spatial structure of the model put in sample sale as the central focus of surrounding foreclosures using the rings and measuring the price impact of foreclosures.

However, reverse-causality bias (or endogeneity) could be a problem if a drop in housing prices in one community is particularly large when compared to another community. This drop could lead to more foreclosures in the given community. If an estimator that does not control for endogeneity of nearby home prices and spatial dependence in this case, the results might overstate or understate the effect of foreclosures on given home prices. The next section will discuss the special methodologies for controlling for endogeneity and spatial dependence.