Academic journal article Cityscape

Does the House or Neighborhood Matter More? Predicting Abandoned Housing Using Multilevel Models

Academic journal article Cityscape

Does the House or Neighborhood Matter More? Predicting Abandoned Housing Using Multilevel Models

Article excerpt

(ProQuest: ... denotes formulae omitted.)


Prediction is a powerful public policy tool. By being able to anticipate phenomena, policymakers are better able to make informed decisions. Given the importance of prediction, researchers often use multivariate regression to predict an outcome (for example, poverty, illness, and foreclosure) based on several potential predictors or causes. Although this method is popular, few researchers have considered the influence that spatial scale might have on their results and model interpretations. Thus, the primary objective of this article is to demonstrate why scale matters; the article does so using an example of abandoned housing prediction. The article will likewise add to the housing literature by providing new information about the spatial characteristics of abandonment. By considering two scales in the same model, one can identify the scale that has the greatest influence on the probability. Perhaps characteristics of a home matter more than the characteristics of the neighborhood where it is located. Some variables might be significant at one scale but not another.

There are many theories about the causes of abandonment. Because the focus of this article is methodological, a literature review on abandonment will not be provided. Nonetheless, the variables and data for this study were adopted from Morckel (2013) who predicted residential abandonment in Columbus, Ohio, using neighborhood-level factors.1 The present study includes information on 120,109 properties in 382 Columbus neighborhoods, with neighborhoods defined as census block groups. The dependent variable is whether a house was identified by city code enforcement as being physically abandoned in 2011, and the independent variables are property values, property sales or transfers, arsons, demolitions, upkeep, property age, tax delinquency, and mortgage foreclosures in 2010. These variables are measured two different ways (at the house and neighborhood levels) to again demonstrate the importance of scale. Exhibit 1 provides additional information on the variables' data sources, measurements, and abbreviations in the forthcoming models.


Unlike traditional regression models, multilevel models enable researchers to predict the probability of a house being abandoned in a particular neighborhood, while taking into account house and neighborhood-level characteristics. Unfortunately, "... social scientists have tended to utilize traditional individual-level statistical tools for their data, even if their data and hypotheses are multilevel in nature" (Luke, 2004: 6). Using traditional methods is problematic with nested data (houses are located within neighborhoods), because not accounting for nesting can result in data dependency and correlated residuals, ultimately biasing regression estimates (Field, 2009). Likewise, it is better for regression analyses that use nested data to take on a multilevel form like the one that follows, for which the j subscripts indicate that a different level-one model is estimated for each of the j leveltwo units (that is, neighborhoods; Luke, 2004). The example is logistic because a house is either abandoned or not.

Level 1-

... (1)

Level 2-


This model differs from a traditional model in that it contains fixed effects (ys) and random effects (us). It is called a random intercepts and slopes (or mixed) model because both the level-one intercepts and slopes are allowed to randomly vary across neighborhoods and are modeled using level-two predictors (Ws). This model form was chosen because previous studies indicated that neighborhoods have different probabilities of abandonment (Morckel, 2014; Morckel, 2013), and it seems plausible that neighborhood-level effects differ by house-level characteristics.

Traditional models are created by entering all predictors into the model at one time or in blocks and removing those that are not statistically significant. …

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