<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-20T00:59:36Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/62104" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/62104</identifier><datestamp>2026-06-06T01:05:37Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">William Wheaton.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Kim, Hyun Jae</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Development.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department" lang="en_US">Massachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Development.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Center for Real Estate</dim:field>
   <dim:field mdschema="dc" element="coverage" qualifier="spatial" lang="en_US">n-us---</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2011-04-04T17:41:45Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2011-04-04T17:41:45Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2010</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2010</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/62104</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">707932517</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (S.M. in Real Estate Development)--Massachusetts Institute of Technology, Program in Real Estate Development in Conjunction with the Center for Real Estate , 2010.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (p. 56).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">The high risk and volatility in the current real estate market has sparked investor interest in understanding what determines real estate market volatility. This study examines the U.S. office markets' overall and decomposed volatilities in vacancy and revenue across 45 metropolitan areas from 1987 to 2010. The relationships of the volatilities with economic and physical market characteristics are also analyzed. The study examines five overall or decomposed market volatilities: volatility in vacancy, volatility in revenue, demand-oriented vacancy change volatility, occupancy-oriented revenue change, and covariance of occupancy rent change. The linear regression analyses are used to explain the movements of the volatilities with market determinants, which include market size, employment growth, jobs in specific industries, submarket structures and geography. This study finds that geographical land availability and employment growth are significantly important for predicting market volatilities. Market size does not affect the decomposed volatility, but it reduces overall vacancy change volatility. Moreover, submarket structure becomes more meaningful when the revenue change volatility is decomposed into occupancy and rent changes. This study gives developers some tools for strategic decision-making in office property development issues.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Hyunjae Kim.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M. in Real Estate Development</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">56 p.</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">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.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Center for Real Estate. Program in Real Estate Development.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Examination of the real estate market risk and volatility : focusing on the U.S. office property</dim:field>
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   	&lt;Title>Examination of the real estate market risk and volatility : focusing on the U.S. office property&lt;/Title>
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   	&lt;PublicationDate>2010&lt;/PublicationDate>
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        	&lt;DisplayName>Kim, Hyun Jae&lt;/DisplayName>
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            &lt;DisplayName>Massachusetts Institute of Technology&lt;/DisplayName>
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    &lt;License>http://dspace.mit.edu/handle/1721.1/7582&lt;/License>
    &lt;Keyword>Center for Real Estate. Program in Real Estate Development.&lt;/Keyword>
   	&lt;Abstract>The high risk and volatility in the current real estate market has sparked investor interest in understanding what determines real estate market volatility. This study examines the U.S. office markets&amp;apos; overall and decomposed volatilities in vacancy and revenue across 45 metropolitan areas from 1987 to 2010. The relationships of the volatilities with economic and physical market characteristics are also analyzed. The study examines five overall or decomposed market volatilities: volatility in vacancy, volatility in revenue, demand-oriented vacancy change volatility, occupancy-oriented revenue change, and covariance of occupancy rent change. The linear regression analyses are used to explain the movements of the volatilities with market determinants, which include market size, employment growth, jobs in specific industries, submarket structures and geography. This study finds that geographical land availability and employment growth are significantly important for predicting market volatilities. Market size does not affect the decomposed volatility, but it reduces overall vacancy change volatility. Moreover, submarket structure becomes more meaningful when the revenue change volatility is decomposed into occupancy and rent changes. This study gives developers some tools for strategic decision-making in office property development issues.&lt;/Abstract>
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