The residential real estate market in Italy: a spatio-temporal analysis

Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical di...
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Autor*in:	Palma, Monica [verfasserIn] Cappello, Claudia [verfasserIn] De Iaco, Sandra [verfasserIn] Pellegrino, Daniela [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Spatio-temporal variogram Kriging predictions Exponential smoothing

Übergeordnetes Werk:	Enthalten in: Quality & quantity - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1967, 53(2018), 5 vom: 30. Mai, Seite 2451-2472
Übergeordnetes Werk:	volume:53 ; year:2018 ; number:5 ; day:30 ; month:05 ; pages:2451-2472

Links:	Volltext

DOI / URN:	10.1007/s11135-018-0768-8

Katalog-ID:	SPR016974034

Internformat


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520			\|a Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed.
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700	1		\|a De Iaco, Sandra \|e verfasserin \|4 aut
700	1		\|a Pellegrino, Daniela \|e verfasserin \|4 aut
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bklnumber	31.00 70.03
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allfields	10.1007/s11135-018-0768-8 doi (DE-627)SPR016974034 (SPR)s11135-018-0768-8-e DE-627 ger DE-627 rakwb eng 050 ASE 31.00 bkl 70.03 bkl Palma, Monica verfasserin aut The residential real estate market in Italy: a spatio-temporal analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed. Spatio-temporal variogram (dpeaa)DE-He213 Kriging predictions (dpeaa)DE-He213 Exponential smoothing (dpeaa)DE-He213 Cappello, Claudia verfasserin aut De Iaco, Sandra verfasserin aut Pellegrino, Daniela verfasserin aut Enthalten in Quality & quantity Dordrecht [u.a.] : Springer Science + Business Media B.V, 1967 53(2018), 5 vom: 30. Mai, Seite 2451-2472 (DE-627)320426874 (DE-600)2003280-8 1573-7845 nnns volume:53 year:2018 number:5 day:30 month:05 pages:2451-2472 https://dx.doi.org/10.1007/s11135-018-0768-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 70.03 ASE AR 53 2018 5 30 05 2451-2472
spelling	10.1007/s11135-018-0768-8 doi (DE-627)SPR016974034 (SPR)s11135-018-0768-8-e DE-627 ger DE-627 rakwb eng 050 ASE 31.00 bkl 70.03 bkl Palma, Monica verfasserin aut The residential real estate market in Italy: a spatio-temporal analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed. Spatio-temporal variogram (dpeaa)DE-He213 Kriging predictions (dpeaa)DE-He213 Exponential smoothing (dpeaa)DE-He213 Cappello, Claudia verfasserin aut De Iaco, Sandra verfasserin aut Pellegrino, Daniela verfasserin aut Enthalten in Quality & quantity Dordrecht [u.a.] : Springer Science + Business Media B.V, 1967 53(2018), 5 vom: 30. Mai, Seite 2451-2472 (DE-627)320426874 (DE-600)2003280-8 1573-7845 nnns volume:53 year:2018 number:5 day:30 month:05 pages:2451-2472 https://dx.doi.org/10.1007/s11135-018-0768-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 70.03 ASE AR 53 2018 5 30 05 2451-2472
allfields_unstemmed	10.1007/s11135-018-0768-8 doi (DE-627)SPR016974034 (SPR)s11135-018-0768-8-e DE-627 ger DE-627 rakwb eng 050 ASE 31.00 bkl 70.03 bkl Palma, Monica verfasserin aut The residential real estate market in Italy: a spatio-temporal analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed. Spatio-temporal variogram (dpeaa)DE-He213 Kriging predictions (dpeaa)DE-He213 Exponential smoothing (dpeaa)DE-He213 Cappello, Claudia verfasserin aut De Iaco, Sandra verfasserin aut Pellegrino, Daniela verfasserin aut Enthalten in Quality & quantity Dordrecht [u.a.] : Springer Science + Business Media B.V, 1967 53(2018), 5 vom: 30. Mai, Seite 2451-2472 (DE-627)320426874 (DE-600)2003280-8 1573-7845 nnns volume:53 year:2018 number:5 day:30 month:05 pages:2451-2472 https://dx.doi.org/10.1007/s11135-018-0768-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 70.03 ASE AR 53 2018 5 30 05 2451-2472
allfieldsGer	10.1007/s11135-018-0768-8 doi (DE-627)SPR016974034 (SPR)s11135-018-0768-8-e DE-627 ger DE-627 rakwb eng 050 ASE 31.00 bkl 70.03 bkl Palma, Monica verfasserin aut The residential real estate market in Italy: a spatio-temporal analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed. Spatio-temporal variogram (dpeaa)DE-He213 Kriging predictions (dpeaa)DE-He213 Exponential smoothing (dpeaa)DE-He213 Cappello, Claudia verfasserin aut De Iaco, Sandra verfasserin aut Pellegrino, Daniela verfasserin aut Enthalten in Quality & quantity Dordrecht [u.a.] : Springer Science + Business Media B.V, 1967 53(2018), 5 vom: 30. Mai, Seite 2451-2472 (DE-627)320426874 (DE-600)2003280-8 1573-7845 nnns volume:53 year:2018 number:5 day:30 month:05 pages:2451-2472 https://dx.doi.org/10.1007/s11135-018-0768-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 70.03 ASE AR 53 2018 5 30 05 2451-2472
allfieldsSound	10.1007/s11135-018-0768-8 doi (DE-627)SPR016974034 (SPR)s11135-018-0768-8-e DE-627 ger DE-627 rakwb eng 050 ASE 31.00 bkl 70.03 bkl Palma, Monica verfasserin aut The residential real estate market in Italy: a spatio-temporal analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed. Spatio-temporal variogram (dpeaa)DE-He213 Kriging predictions (dpeaa)DE-He213 Exponential smoothing (dpeaa)DE-He213 Cappello, Claudia verfasserin aut De Iaco, Sandra verfasserin aut Pellegrino, Daniela verfasserin aut Enthalten in Quality & quantity Dordrecht [u.a.] : Springer Science + Business Media B.V, 1967 53(2018), 5 vom: 30. Mai, Seite 2451-2472 (DE-627)320426874 (DE-600)2003280-8 1573-7845 nnns volume:53 year:2018 number:5 day:30 month:05 pages:2451-2472 https://dx.doi.org/10.1007/s11135-018-0768-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE 70.03 ASE AR 53 2018 5 30 05 2451-2472
language	English
source	Enthalten in Quality & quantity 53(2018), 5 vom: 30. Mai, Seite 2451-2472 volume:53 year:2018 number:5 day:30 month:05 pages:2451-2472
sourceStr	Enthalten in Quality & quantity 53(2018), 5 vom: 30. Mai, Seite 2451-2472 volume:53 year:2018 number:5 day:30 month:05 pages:2451-2472
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Spatio-temporal variogram Kriging predictions Exponential smoothing
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container_title	Quality & quantity
authorswithroles_txt_mv	Palma, Monica @@aut@@ Cappello, Claudia @@aut@@ De Iaco, Sandra @@aut@@ Pellegrino, Daniela @@aut@@
publishDateDaySort_date	2018-05-30T00:00:00Z
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author	Palma, Monica
spellingShingle	Palma, Monica ddc 050 bkl 31.00 bkl 70.03 misc Spatio-temporal variogram misc Kriging predictions misc Exponential smoothing The residential real estate market in Italy: a spatio-temporal analysis
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topic_title	050 ASE 31.00 bkl 70.03 bkl The residential real estate market in Italy: a spatio-temporal analysis Spatio-temporal variogram (dpeaa)DE-He213 Kriging predictions (dpeaa)DE-He213 Exponential smoothing (dpeaa)DE-He213
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title	The residential real estate market in Italy: a spatio-temporal analysis
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title_full	The residential real estate market in Italy: a spatio-temporal analysis
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title_sort	residential real estate market in italy: a spatio-temporal analysis
title_auth	The residential real estate market in Italy: a spatio-temporal analysis
abstract	Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed.
abstractGer	Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed.
abstract_unstemmed	Abstract Real estate market follows generally the evolution of the economic cycles. In Italy, it has been characterized by a period of expansion (2000–2006) and a period of regression (2007–2013), with a slow growth in the last years, which reflects essentially the European context. The empirical distribution of the number of normalized transactions (NNT), registered in the residential real estate market, provides a realistic picture of the Italian market and reveals several differences among the Northern, Central and Southern parts of the nation. Although time series analysis has been widely used to describe the temporal evolution of socio-economic variables, recently increasing attention has been also given to the study of the spatial distribution of these phenomena. The aim of the paper is to propose a geostatistical model for describing and predicting simultaneously the spatial and temporal evolution of the Italian NNT. After discussing the systematic variations of the NNT, the residuals at local level, which are characterized by a significant spatio-temporal correlation, are studied and modeled. The convenience of using a spatio-temporal model versus an only temporal model is analyzed.
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container_issue	5
title_short	The residential real estate market in Italy: a spatio-temporal analysis
url	https://dx.doi.org/10.1007/s11135-018-0768-8
remote_bool	true
author2	Cappello, Claudia De Iaco, Sandra Pellegrino, Daniela
author2Str	Cappello, Claudia De Iaco, Sandra Pellegrino, Daniela
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doi_str	10.1007/s11135-018-0768-8
up_date	2024-07-04T01:40:51.120Z
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