Hybrid multilevel STAR models for hedonic house prices View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-10

AUTHORS

Wolfgang A. Brunauer, Stefan Lang, Wolfgang Feilmayr

ABSTRACT

This article proposes a procedure that exploits two heterogeneous data sets to derive spatial house price predictions for Austria in a semiparametric hierarchical regression framework. The first data set contains a large number of house price data, but only a small number of house characteristics (a long data set), giving rise to the omitted variable bias. In contrast, the second data set contains a small number of observations with a wide range of house price attributes (a wide data set). Although the latter allows for detailed estimation of the effects of house price characteristics, the sparse and uneven distribution over the research area leads to volatility in spatial effects. To circumvent these disadvantages, we propose a two-step approach: In the first step, we model the long data set, being aware of potential bias in the estimated effects. We apply a multilevel version of structured additive regression (STAR) models in order to account for nonlinearity in price functions as well as hierarchical spatial heterogeneity not explicitly modeled by covariates. The spatial prediction from this model is used as an explanatory covariate for the wide price data in the second stage. This novel modeling approach, a hybrid multilevel STAR model, avoids the bias from omitted variables on the one hand and yields robust spatial predictions on the other hand. We present detailed comparisons of nonlinear covariate effects and spatial heterogeneity from both stages, contrasting them with estimated effects from a reference model that is only based on the wide data set. The presented results support the approach taken in this paper, which proves particularly useful for spatial prediction of house prices. More... »

PAGES

151-172

References to SciGraph publications

  • 1998-07. Analysis of Spatial Autocorrelation in House Prices in THE JOURNAL OF REAL ESTATE FINANCE AND ECONOMICS
  • 1988. Spatial Econometrics: Methods and Models in NONE
  • Journal

    TITLE

    Review of Regional Research

    ISSUE

    2

    VOLUME

    33

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10037-013-0074-9

    DOI

    http://dx.doi.org/10.1007/s10037-013-0074-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1041987539


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1403", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Econometrics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Economics", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "name": [
                "UniCredit Bank Austria AG, Julius Tandler-Platz 3, 1090, Vienna, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Brunauer", 
            "givenName": "Wolfgang A.", 
            "id": "sg:person.015657455455.48", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015657455455.48"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "University of Innsbruck, Universit\u00e4tsstr. 15, 6020, Innsbruck, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lang", 
            "givenName": "Stefan", 
            "id": "sg:person.010223676761.81", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010223676761.81"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Techncal University of Vienna, Operngasse 11, 1040, Vienna, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Feilmayr", 
            "givenName": "Wolfgang", 
            "id": "sg:person.016411005764.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016411005764.46"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/0166-0462(85)90033-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009108607"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0166-0462(85)90033-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009108607"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/9780470690680.ch5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009192364"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.csda.2004.10.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015498897"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-94-015-7799-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030890521", 
              "https://doi.org/10.1007/978-94-015-7799-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-94-015-7799-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030890521", 
              "https://doi.org/10.1007/978-94-015-7799-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0160017602250977", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034192437"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0160017602250977", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034192437"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/ss/1038425655", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041521657"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/1467-9868.00353", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047288536"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1007703229507", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047729753", 
              "https://doi.org/10.1023/a:1007703229507"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0160017602250972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052413944"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0160017602250972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052413944"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1086/259131", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058572612"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1086/260169", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058573650"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/biomet/82.4.733", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059420612"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1471082x13480650", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064025804"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1471082x13480650", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064025804"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.18637/jss.v014.i11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1068672215"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2013-10", 
        "datePublishedReg": "2013-10-01", 
        "description": "This article proposes a procedure that exploits two heterogeneous data sets to derive spatial house price predictions for Austria in a semiparametric hierarchical regression framework. The first data set contains a large number of house price data, but only a small number of house characteristics (a long data set), giving rise to the omitted variable bias. In contrast, the second data set contains a small number of observations with a wide range of house price attributes (a wide data set). Although the latter allows for detailed estimation of the effects of house price characteristics, the sparse and uneven distribution over the research area leads to volatility in spatial effects. To circumvent these disadvantages, we propose a two-step approach: In the first step, we model the long data set, being aware of potential bias in the estimated effects. We apply a multilevel version of structured additive regression (STAR) models in order to account for nonlinearity in price functions as well as hierarchical spatial heterogeneity not explicitly modeled by covariates. The spatial prediction from this model is used as an explanatory covariate for the wide price data in the second stage. This novel modeling approach, a hybrid multilevel STAR model, avoids the bias from omitted variables on the one hand and yields robust spatial predictions on the other hand. We present detailed comparisons of nonlinear covariate effects and spatial heterogeneity from both stages, contrasting them with estimated effects from a reference model that is only based on the wide data set. The presented results support the approach taken in this paper, which proves particularly useful for spatial prediction of house prices.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10037-013-0074-9", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1136028", 
            "issn": [
              "0173-7600", 
              "1613-9836"
            ], 
            "name": "Review of Regional Research", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "33"
          }
        ], 
        "name": "Hybrid multilevel STAR models for hedonic house prices", 
        "pagination": "151-172", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "c8025990acbbede80f81e920e47d01101ef255235372657f0f98046fddcf6a61"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10037-013-0074-9"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1041987539"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10037-013-0074-9", 
          "https://app.dimensions.ai/details/publication/pub.1041987539"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T22:27", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8690_00000490.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/s10037-013-0074-9"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10037-013-0074-9'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10037-013-0074-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10037-013-0074-9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10037-013-0074-9'


     

    This table displays all metadata directly associated to this object as RDF triples.

    122 TRIPLES      21 PREDICATES      41 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10037-013-0074-9 schema:about anzsrc-for:14
    2 anzsrc-for:1403
    3 schema:author N2638163762a14be8bc80fcb08147f813
    4 schema:citation sg:pub.10.1007/978-94-015-7799-1
    5 sg:pub.10.1023/a:1007703229507
    6 https://doi.org/10.1002/9780470690680.ch5
    7 https://doi.org/10.1016/0166-0462(85)90033-x
    8 https://doi.org/10.1016/j.csda.2004.10.011
    9 https://doi.org/10.1086/259131
    10 https://doi.org/10.1086/260169
    11 https://doi.org/10.1093/biomet/82.4.733
    12 https://doi.org/10.1111/1467-9868.00353
    13 https://doi.org/10.1177/0160017602250972
    14 https://doi.org/10.1177/0160017602250977
    15 https://doi.org/10.1177/1471082x13480650
    16 https://doi.org/10.1214/ss/1038425655
    17 https://doi.org/10.18637/jss.v014.i11
    18 schema:datePublished 2013-10
    19 schema:datePublishedReg 2013-10-01
    20 schema:description This article proposes a procedure that exploits two heterogeneous data sets to derive spatial house price predictions for Austria in a semiparametric hierarchical regression framework. The first data set contains a large number of house price data, but only a small number of house characteristics (a long data set), giving rise to the omitted variable bias. In contrast, the second data set contains a small number of observations with a wide range of house price attributes (a wide data set). Although the latter allows for detailed estimation of the effects of house price characteristics, the sparse and uneven distribution over the research area leads to volatility in spatial effects. To circumvent these disadvantages, we propose a two-step approach: In the first step, we model the long data set, being aware of potential bias in the estimated effects. We apply a multilevel version of structured additive regression (STAR) models in order to account for nonlinearity in price functions as well as hierarchical spatial heterogeneity not explicitly modeled by covariates. The spatial prediction from this model is used as an explanatory covariate for the wide price data in the second stage. This novel modeling approach, a hybrid multilevel STAR model, avoids the bias from omitted variables on the one hand and yields robust spatial predictions on the other hand. We present detailed comparisons of nonlinear covariate effects and spatial heterogeneity from both stages, contrasting them with estimated effects from a reference model that is only based on the wide data set. The presented results support the approach taken in this paper, which proves particularly useful for spatial prediction of house prices.
    21 schema:genre research_article
    22 schema:inLanguage en
    23 schema:isAccessibleForFree false
    24 schema:isPartOf N4528705c3dae4ef6aeb698bf6b68749e
    25 N9f4022e53b014e829ed7fb02706d5bf5
    26 sg:journal.1136028
    27 schema:name Hybrid multilevel STAR models for hedonic house prices
    28 schema:pagination 151-172
    29 schema:productId N990038f02576487c956c69ca0c3f55fe
    30 Na7a5bd58ca26423db75478fde61a6282
    31 Ndff486d1b85d45789ae39221e4c9f674
    32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041987539
    33 https://doi.org/10.1007/s10037-013-0074-9
    34 schema:sdDatePublished 2019-04-10T22:27
    35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    36 schema:sdPublisher N718104fdb5ad4f588c69a46c43823553
    37 schema:url http://link.springer.com/10.1007/s10037-013-0074-9
    38 sgo:license sg:explorer/license/
    39 sgo:sdDataset articles
    40 rdf:type schema:ScholarlyArticle
    41 N2638163762a14be8bc80fcb08147f813 rdf:first sg:person.015657455455.48
    42 rdf:rest Nd1b87efbe86048699d184b4bbe1b56bc
    43 N284e7b17430b493ba82bff7a0a6a425b schema:name University of Innsbruck, Universitätsstr. 15, 6020, Innsbruck, Austria
    44 rdf:type schema:Organization
    45 N407a06de4b9845ca9efc51abec00fc06 schema:name Techncal University of Vienna, Operngasse 11, 1040, Vienna, Austria
    46 rdf:type schema:Organization
    47 N4528705c3dae4ef6aeb698bf6b68749e schema:volumeNumber 33
    48 rdf:type schema:PublicationVolume
    49 N5697140f0f8641758e9a38995f02bfd3 rdf:first sg:person.016411005764.46
    50 rdf:rest rdf:nil
    51 N6b2dd2451b814536802cd5164b511cec schema:name UniCredit Bank Austria AG, Julius Tandler-Platz 3, 1090, Vienna, Austria
    52 rdf:type schema:Organization
    53 N718104fdb5ad4f588c69a46c43823553 schema:name Springer Nature - SN SciGraph project
    54 rdf:type schema:Organization
    55 N990038f02576487c956c69ca0c3f55fe schema:name readcube_id
    56 schema:value c8025990acbbede80f81e920e47d01101ef255235372657f0f98046fddcf6a61
    57 rdf:type schema:PropertyValue
    58 N9f4022e53b014e829ed7fb02706d5bf5 schema:issueNumber 2
    59 rdf:type schema:PublicationIssue
    60 Na7a5bd58ca26423db75478fde61a6282 schema:name dimensions_id
    61 schema:value pub.1041987539
    62 rdf:type schema:PropertyValue
    63 Nd1b87efbe86048699d184b4bbe1b56bc rdf:first sg:person.010223676761.81
    64 rdf:rest N5697140f0f8641758e9a38995f02bfd3
    65 Ndff486d1b85d45789ae39221e4c9f674 schema:name doi
    66 schema:value 10.1007/s10037-013-0074-9
    67 rdf:type schema:PropertyValue
    68 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
    69 schema:name Economics
    70 rdf:type schema:DefinedTerm
    71 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
    72 schema:name Econometrics
    73 rdf:type schema:DefinedTerm
    74 sg:journal.1136028 schema:issn 0173-7600
    75 1613-9836
    76 schema:name Review of Regional Research
    77 rdf:type schema:Periodical
    78 sg:person.010223676761.81 schema:affiliation N284e7b17430b493ba82bff7a0a6a425b
    79 schema:familyName Lang
    80 schema:givenName Stefan
    81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010223676761.81
    82 rdf:type schema:Person
    83 sg:person.015657455455.48 schema:affiliation N6b2dd2451b814536802cd5164b511cec
    84 schema:familyName Brunauer
    85 schema:givenName Wolfgang A.
    86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015657455455.48
    87 rdf:type schema:Person
    88 sg:person.016411005764.46 schema:affiliation N407a06de4b9845ca9efc51abec00fc06
    89 schema:familyName Feilmayr
    90 schema:givenName Wolfgang
    91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016411005764.46
    92 rdf:type schema:Person
    93 sg:pub.10.1007/978-94-015-7799-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030890521
    94 https://doi.org/10.1007/978-94-015-7799-1
    95 rdf:type schema:CreativeWork
    96 sg:pub.10.1023/a:1007703229507 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047729753
    97 https://doi.org/10.1023/a:1007703229507
    98 rdf:type schema:CreativeWork
    99 https://doi.org/10.1002/9780470690680.ch5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009192364
    100 rdf:type schema:CreativeWork
    101 https://doi.org/10.1016/0166-0462(85)90033-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1009108607
    102 rdf:type schema:CreativeWork
    103 https://doi.org/10.1016/j.csda.2004.10.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015498897
    104 rdf:type schema:CreativeWork
    105 https://doi.org/10.1086/259131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058572612
    106 rdf:type schema:CreativeWork
    107 https://doi.org/10.1086/260169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058573650
    108 rdf:type schema:CreativeWork
    109 https://doi.org/10.1093/biomet/82.4.733 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059420612
    110 rdf:type schema:CreativeWork
    111 https://doi.org/10.1111/1467-9868.00353 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047288536
    112 rdf:type schema:CreativeWork
    113 https://doi.org/10.1177/0160017602250972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052413944
    114 rdf:type schema:CreativeWork
    115 https://doi.org/10.1177/0160017602250977 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034192437
    116 rdf:type schema:CreativeWork
    117 https://doi.org/10.1177/1471082x13480650 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064025804
    118 rdf:type schema:CreativeWork
    119 https://doi.org/10.1214/ss/1038425655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041521657
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.18637/jss.v014.i11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068672215
    122 rdf:type schema:CreativeWork
     




    Preview window. Press ESC to close (or click here)


    ...