Preprint Open Access

Baltimore Housing Prices Disparity for Comparable Neighborhoods: A Case for Enabling Interactive,Visual Exploration of Neighborhoods

Peshave, Akshay; Memon, Siraj; Chavan, Vedmurtty; Oates, Tim


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.884488</identifier>
  <creators>
    <creator>
      <creatorName>Peshave, Akshay</creatorName>
      <givenName>Akshay</givenName>
      <familyName>Peshave</familyName>
      <affiliation>University of Maryland</affiliation>
    </creator>
    <creator>
      <creatorName>Memon, Siraj</creatorName>
      <givenName>Siraj</givenName>
      <familyName>Memon</familyName>
      <affiliation>University of Maryland</affiliation>
    </creator>
    <creator>
      <creatorName>Chavan, Vedmurtty</creatorName>
      <givenName>Vedmurtty</givenName>
      <familyName>Chavan</familyName>
      <affiliation>University of Maryland</affiliation>
    </creator>
    <creator>
      <creatorName>Oates, Tim</creatorName>
      <givenName>Tim</givenName>
      <familyName>Oates</familyName>
      <affiliation>University of Maryland</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Baltimore Housing Prices Disparity for Comparable Neighborhoods: A Case for Enabling Interactive,Visual Exploration of Neighborhoods</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>correlation analysis</subject>
    <subject>clustering</subject>
    <subject>data visualisation</subject>
    <subject>Baltimore city</subject>
    <subject>housing and community development</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-09-04</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Preprint</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/884488</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.884487</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/dfp17</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;As government agencies increasingly make public data available online, it provides opportunities to leverage such data for descriptive, predictive and prescriptive analytics. One domain where these technological capabilities are applicable is real-estate development and housing market domain. This domain is of interest to home buyers, investors and policy makers. Diverse and varying preferences of residents of a geography are latent behavioral factors that affect residential property prices. This paper describes a geographical area agnostic housing typology classifier for Baltimore City communities or neighborhoods. Further, it discussed correlation analysis and composite Vital Signs scores to characterize city population perceptions of different community development categories. These scores enable community clustering to investigate price disparity in comparable communities based on configurable categories and year-on-year trend analysis. Various visualization possibilities are discussed in conjunction with these approaches to make a case for interactive, visual exploration of geographical communities which may be extended to comparative analysis across geographies. &lt;/p&gt;</description>
  </descriptions>
</resource>
163
58
views
downloads
All versions This version
Views 163162
Downloads 5858
Data volume 20.5 MB20.5 MB
Unique views 161160
Unique downloads 5252

Share

Cite as