Retail real estate analysis is the most well developed, complex and technological of any of the other real estate product categories. This chapter begins with a review of the background literature. Real estate market analysis has been performed for retail longer than for any other category. Unlike medicine, in which some practices, such as bleeding a patient, have become obsolete, no major method that has been widely adopted by the industry during the past 100 years has subsequently gone out of use. Instead, these methods have been modified and incorporated into contemporary analysis and technology. This chapter next proceeds to the macro level with a presentation of how a real estate location strategy is developed for a large, multibranch retail chain. Afterward, a brief discussion of the micro-level strategy is presented following the four steps introduced in the preceding chapters. The methods, technology, and analysis of the four steps for retail real estate have already been presented in chapter 4. Because the retail side has been such a pioneer, methods developed for retail ultimately find use, with some modification, in real estate market analysis of all the other real estate product categories; hence, the four steps were presented as part of the general methods of chapter 4. It has always been the case that certain locations for retail activities offer distinct advantages over other locations. But knowing which locations were best has not al-ways been the complex task it is today. Until the late nineteenth century, the retail location decision was quite simple: always locate at the downtown commercial node. Changes in transportation technology increased the geographic range of the individual household. Each successive transportation era brought increasing geodemographic complexity to the city. The eras of transportation can be broken down by transportation mode: . . . First, households relied primarily on walking from home to work to shopping. Second, for some of the larger cities, the trolley car and other innovations in public transit brought an increased geographic reach of the average household. Third, the personal automobile allowed many households to move beyond the limited corridors of public transit.
Comparing prediction algorithms in disorganized data