Impact of Cliff and Ord on the Housing and Real Estate Literature R. Kelley Pace 1 , James LeSage 2 , Shuang Zhu 1 1 Department of Finance, Louisiana State University, Baton Rouge, LA, 2 Texas State University, San Marcos, TX The works of Cliff and Ord have had a major impact on empirical practices in real estate. Cliff and Ord proposed both techniques for detecting as well as modeling spatial dependence. Because the existence of spatial dependence is almost assured in real estate data, their most important contribution was feasible means of estimating spatial models. The full implications of these ideas and the numerous modeling techniques spawned by their seminal works have not been fully explored and provide numerous opportunities for future research. Introduction Spatial ideas have always been fundamental to real estate and housing. However, the need for simplicity in theory and statistical analysis in early work led to distilling the two dimensions of space into a single dimension distance (such as from each home location to the urban center). Ideally, a regression containing a distance variable would yield residuals that show no obvious spatial patterns. Because often this did not occur in practice, researchers sometimes included distances to other points, regional or neighborhood indicator variables, polynomi- als in the locational coordinates, and other trend surfaces in an effort to reduce the obvious map patterns in the residuals. Even after controlling for space in this fash- ion, samples containing a large number of nearby homes usually exhibit spatial clusters of regression residuals with the same sign. This outcome is because pairs of nearby houses lie in the same neighborhoods so that neighborhood indicator vari- ables do not treat these observations differently. In addition, trend surfaces change little over short distances so that these variables provide little gain in explanatory power, and distances to other locations are virtually the same for pairs of neigh- boring houses. We now know that these standard statistical techniques were based on assumed independence among sample observations, which real estate and housing data violate. In the face of sample data inconsistent with independence, Correspondence: R. Kelley Pace, Department of Finance, Louisiana State University, LREC Endowed Chair of Real Estate, Baton Rouge, LA 70803-6308 e-mail: kelley@pace.am Geographical Analysis 41 (2009) 418–424 r 2009 The Ohio State University 418 Geographical Analysis ISSN 0016-7363