Retail sales forecast : a cross sectional approach for real investment strategy

Abstract

The intent of this thesis is to identify the demand drivers for ten retail sub-categories in the US and develop an understanding of how to best use this information to make better retail real estate investment decisions. This cross sectional study analyzes sales per population, establishment per population, and sales per establishment based on six independent variables and the 2002 data set of 54 metropolitan statistical areas. The independent variables are population, employment per population, income per population, precipitation, temperature, and population growth. The first portion of this thesis is to analyze the demand drivers for each retail category and the degree of effectiveness of each variable on retail sales performance. The regression results of this study have clearly demonstrated a measurable demand for each retail category given the nature of each product type. The last aspect of this thesis is the development of an investment strategy that examines the predicted results versus the actual sales figures to see if a certain city is over saturated or under-supplied with retail establishments by category. By understanding what is the exact demand driver for each category, real estate investors are able to use this information efficiently to make informed investment decisions based on demand drivers as well as retail store supplies. This methodology provides a reasonable and well thought-out strategy to avoid unsuccessful investment outcomes.