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William C. Wheaton. Kong, Ai, S.M. Massachusetts Institute of Technology Massachusetts Institute of Technology. Center for Real Estate. Massachusetts Institute of Technology. Center for Real Estate Massachusetts Institute of Technology. Department of Architecture 2010-09-22T15:55:52Z 2010-09-22T15:55:52Z 2008 2008 http://hdl.handle.net/1721.1/58636 314865418 Thesis (S.M. in Real Estate Development)--Massachusetts Institute of Technology, Dept. of Architecture, Center for Real Estate, 2008. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (leaves 56-57). 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. by Ai Kong. S.M. in Real Estate Development 57 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Architecture. Center for Real Estate. Retail sales forecast : a cross sectional approach for real investment strategy Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e5a2ac08-b77b-4597-ac60-f53c4454f46c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Retail sales forecast : a cross sectional approach for real investment strategy</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2008</PublicationDate> <Authors> <Author> <DisplayName>Kong, Ai, S.M. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Architecture.</Keyword> <Keyword>Center for Real Estate.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>