Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations
The authors use a data set of over 20,000 residential housing sales in 2010 and 2011 in San Diego County to measure the school proximity effects on nearby residential housing. Prior research supports the existence of a “school proximity premium” for housing located near K-12 schools. However, since...
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description | The authors use a data set of over 20,000 residential housing sales in 2010 and 2011 in San Diego County to measure the school proximity effects on nearby residential housing. Prior research supports the existence of a “school proximity premium” for housing located near K-12 schools. However, since these investigations have lacked important spatial controls, the authors investigate whether their inclusion affects outcomes. In addition, the authors separate out the effects for proximity to public and private elementary schools, measure the distance from schools using a more precise distance measurement and utilize a very large data set on the West Coast of the US. Counter to prior investigations, the authors find strong evidence for a “school proximity penalty,” for public elementary schools suggesting that proximity is perceived to be a net negative for homebuyers in San Diego County. When spatially dividing the sample area into a coastal and inland region, the authors find the results for the inland region for public schools more closely resemble the results from prior investigations (with a “delayed” proximity premium) while the coastal results demonstrate a very strong “proximity penalty” effect. Results presented here indicate that failure to include robust spatial controls (including zip code and census tract fixed effects) could have important implications for hedonic housing price estimates of this kind. |
doi_str_mv | 10.1007/s11146-015-9520-5 |
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Prior research supports the existence of a “school proximity premium” for housing located near K-12 schools. However, since these investigations have lacked important spatial controls, the authors investigate whether their inclusion affects outcomes. In addition, the authors separate out the effects for proximity to public and private elementary schools, measure the distance from schools using a more precise distance measurement and utilize a very large data set on the West Coast of the US. Counter to prior investigations, the authors find strong evidence for a “school proximity penalty,” for public elementary schools suggesting that proximity is perceived to be a net negative for homebuyers in San Diego County. When spatially dividing the sample area into a coastal and inland region, the authors find the results for the inland region for public schools more closely resemble the results from prior investigations (with a “delayed” proximity premium) while the coastal results demonstrate a very strong “proximity penalty” effect. Results presented here indicate that failure to include robust spatial controls (including zip code and census tract fixed effects) could have important implications for hedonic housing price estimates of this kind.</description><identifier>ISSN: 0895-5638</identifier><identifier>EISSN: 1573-045X</identifier><identifier>DOI: 10.1007/s11146-015-9520-5</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Censuses ; Coasts ; Costs ; Datasets ; Economic models ; Economic statistics ; Economic theory ; Economics ; Economics and Finance ; Elementary schools ; Financial Services ; Geography ; Home buyers ; Housing ; Housing prices ; Noise ; Public schools ; Real estate ; Real estate sales ; Regional/Spatial Science ; School districts ; Schools ; Studies ; Traffic congestion ; Vandalism</subject><ispartof>The journal of real estate finance and economics, 2016-07, Vol.53 (1), p.50-76</ispartof><rights>Springer Science+Business Media New York 2015</rights><rights>Springer Science+Business Media New York 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-db6657d2d168c42ee74c2bc23c01216afbd19bb68f7909304c1e42198baea49f3</citedby><cites>FETCH-LOGICAL-c347t-db6657d2d168c42ee74c2bc23c01216afbd19bb68f7909304c1e42198baea49f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11146-015-9520-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11146-015-9520-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Sah, Vivek</creatorcontrib><creatorcontrib>Conroy, Stephen J.</creatorcontrib><creatorcontrib>Narwold, Andrew</creatorcontrib><title>Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations</title><title>The journal of real estate finance and economics</title><addtitle>J Real Estate Finan Econ</addtitle><description>The authors use a data set of over 20,000 residential housing sales in 2010 and 2011 in San Diego County to measure the school proximity effects on nearby residential housing. Prior research supports the existence of a “school proximity premium” for housing located near K-12 schools. However, since these investigations have lacked important spatial controls, the authors investigate whether their inclusion affects outcomes. In addition, the authors separate out the effects for proximity to public and private elementary schools, measure the distance from schools using a more precise distance measurement and utilize a very large data set on the West Coast of the US. Counter to prior investigations, the authors find strong evidence for a “school proximity penalty,” for public elementary schools suggesting that proximity is perceived to be a net negative for homebuyers in San Diego County. When spatially dividing the sample area into a coastal and inland region, the authors find the results for the inland region for public schools more closely resemble the results from prior investigations (with a “delayed” proximity premium) while the coastal results demonstrate a very strong “proximity penalty” effect. Results presented here indicate that failure to include robust spatial controls (including zip code and census tract fixed effects) could have important implications for hedonic housing price estimates of this kind.</description><subject>Censuses</subject><subject>Coasts</subject><subject>Costs</subject><subject>Datasets</subject><subject>Economic models</subject><subject>Economic statistics</subject><subject>Economic theory</subject><subject>Economics</subject><subject>Economics and Finance</subject><subject>Elementary schools</subject><subject>Financial Services</subject><subject>Geography</subject><subject>Home buyers</subject><subject>Housing</subject><subject>Housing prices</subject><subject>Noise</subject><subject>Public schools</subject><subject>Real estate</subject><subject>Real estate sales</subject><subject>Regional/Spatial Science</subject><subject>School districts</subject><subject>Schools</subject><subject>Studies</subject><subject>Traffic congestion</subject><subject>Vandalism</subject><issn>0895-5638</issn><issn>1573-045X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kEFLwzAYhoMoOKc_wFvAczVf2iStNxnTDQYOp-AttGmydXRNTTJ0_96MKnjx9F2e930_HoSugdwCIeLOA0DGEwIsKRglCTtBI2AiTUjG3k_RiOQFSxhP83N04f2WEMJFTkboc-pDsytD063xSm2sbfHS2a9m14QDnhqjVfDYdnhm9_7ILF2jtL_HYaPxfNdbF8pOaWwNfrHV3ge86mNZ2eKJ7YKzrcdNDOvado3Cv1u285fozJSt11c_d4zeHqevk1myeH6aTx4WiUozEZK64pyJmtbAc5VRrUWmaKVoqghQ4KWpaiiqiudGFKRISaZAZxSKvCp1mRUmHaObobd39mOvfZBbu3ddnJQgCipItJVHCgZKOeu900b2Ln7qDhKIPPqVg18Z_cqjX8lihg4ZH9lurd2f5n9D36vofws</recordid><startdate>20160701</startdate><enddate>20160701</enddate><creator>Sah, Vivek</creator><creator>Conroy, Stephen J.</creator><creator>Narwold, Andrew</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>885</scope><scope>8AO</scope><scope>8BJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ANIOZ</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRAZJ</scope><scope>FRNLG</scope><scope>F~G</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>M0C</scope><scope>M1F</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20160701</creationdate><title>Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations</title><author>Sah, Vivek ; Conroy, Stephen J. ; Narwold, Andrew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-db6657d2d168c42ee74c2bc23c01216afbd19bb68f7909304c1e42198baea49f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Censuses</topic><topic>Coasts</topic><topic>Costs</topic><topic>Datasets</topic><topic>Economic models</topic><topic>Economic statistics</topic><topic>Economic theory</topic><topic>Economics</topic><topic>Economics and Finance</topic><topic>Elementary schools</topic><topic>Financial Services</topic><topic>Geography</topic><topic>Home buyers</topic><topic>Housing</topic><topic>Housing prices</topic><topic>Noise</topic><topic>Public schools</topic><topic>Real estate</topic><topic>Real estate sales</topic><topic>Regional/Spatial Science</topic><topic>School districts</topic><topic>Schools</topic><topic>Studies</topic><topic>Traffic congestion</topic><topic>Vandalism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sah, Vivek</creatorcontrib><creatorcontrib>Conroy, Stephen J.</creatorcontrib><creatorcontrib>Narwold, Andrew</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Banking Information Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Accounting, Tax & Banking Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Accounting, Tax & Banking Collection (Alumni)</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Banking Information Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>The journal of real estate finance and economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sah, Vivek</au><au>Conroy, Stephen J.</au><au>Narwold, Andrew</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations</atitle><jtitle>The journal of real estate finance and economics</jtitle><stitle>J Real Estate Finan Econ</stitle><date>2016-07-01</date><risdate>2016</risdate><volume>53</volume><issue>1</issue><spage>50</spage><epage>76</epage><pages>50-76</pages><issn>0895-5638</issn><eissn>1573-045X</eissn><abstract>The authors use a data set of over 20,000 residential housing sales in 2010 and 2011 in San Diego County to measure the school proximity effects on nearby residential housing. Prior research supports the existence of a “school proximity premium” for housing located near K-12 schools. However, since these investigations have lacked important spatial controls, the authors investigate whether their inclusion affects outcomes. In addition, the authors separate out the effects for proximity to public and private elementary schools, measure the distance from schools using a more precise distance measurement and utilize a very large data set on the West Coast of the US. Counter to prior investigations, the authors find strong evidence for a “school proximity penalty,” for public elementary schools suggesting that proximity is perceived to be a net negative for homebuyers in San Diego County. When spatially dividing the sample area into a coastal and inland region, the authors find the results for the inland region for public schools more closely resemble the results from prior investigations (with a “delayed” proximity premium) while the coastal results demonstrate a very strong “proximity penalty” effect. Results presented here indicate that failure to include robust spatial controls (including zip code and census tract fixed effects) could have important implications for hedonic housing price estimates of this kind.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11146-015-9520-5</doi><tpages>27</tpages></addata></record> |
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subjects | Censuses Coasts Costs Datasets Economic models Economic statistics Economic theory Economics Economics and Finance Elementary schools Financial Services Geography Home buyers Housing Housing prices Noise Public schools Real estate Real estate sales Regional/Spatial Science School districts Schools Studies Traffic congestion Vandalism |
title | Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations |
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