<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>The Research Department of Economics and Management of Tadbir Nikan</PublisherName>
      <JournalTitle>Business, Marketing, and Finance Open</JournalTitle>
      <Issn>3092-6238</Issn>
      <Volume></Volume>
      <Issue>In Press</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>09</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Development of a Dynamic Spatial Durbin Model with Generalized Common Effects (SDM-DPD(GCE)) to Examine the Impact of Macroeconomic Indicators on Housing Prices in Iran</ArticleTitle>
    <VernacularTitle>Development of a Dynamic Spatial Durbin Model with Generalized Common Effects (SDM-DPD(GCE)) to Examine the Impact of Macroeconomic Indicators on Housing Prices in Iran</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>19</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2026</Year>
        <Month>01</Month>
        <Day>01</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;Regional housing prices in emerging economies are simultaneously shaped by common macroeconomic shocks, geographic spillovers and local speculative behaviour. Jointly assessing their relative roles within a single framework is the aim of this paper. We study Iran’s 31 provinces over 2015–2024 using a Dynamic Spatial Durbin Model with Generalised Common Effects (SDM-DPD(GCE)), estimated by Quasi-Maximum Likelihood. After removing the dominant common macro factor, a spatial ripple coefficient of 0.593 emerges, consistent with cross-border price linkages, conditional on the maintained model specification, rather than shared exposure to national shocks. A lagged provincial herding proxy (CSAD) is associated with further price divergence the following year, with a total effect of 1.721 once spatial feedback is accounted for. A structural break at 2018 marks a sharp intensification of both dynamics, coinciding with Iran’s major currency shock. The paper also provides empirical evidence consistent with the view that extreme cross-sectional dependence can suppress Moran’s I in raw panels, a diagnostic pitfall relevant to any high-CD empirical setting.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Housing prices</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Spatial spillovers</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Herding behavior</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Cross-sectional dependence</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Dynamic spatial econometrics</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Generalized common effects</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Iran</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Emerging markets</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://bmfopen.com/index.php/bmfopen/article/download/437/338</ArchiveCopySource>
  </Article>
</ArticleSet>
