The Potential of Big Housing Data: An Application to the Italian Real-Estate Market

Loberto, Michele; Luciani, Andrea; Pangallo, Marco

doi:10.2139/ssrn.3176962

Cited by 23 publications

(24 citation statements)

References 34 publications

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“…Baptista et al (2016) also use λ = 0.95, obtained from Zoopla data (Zoopla is a popular online portal for real-estate services in England). Finally, Loberto et al (2018) also find a similar value from the analysis of a housing advertisements website in Italy. • Γ = 1000, N = 100, α = 0.1.…”

Section: Parameter Valuessupporting

confidence: 68%

Residential Income Segregation: A Behavioral Model of the Housing Market

2017

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We represent the functioning of the housing market and the processes that shape residential income segregation by introducing a spatial Agent-Based Model (ABM). Differently from traditional models in urban economics, we explicitly specify the behavior of buyers and sellers and the price formation mechanism. Buyers who differ by income select among heterogeneous neighborhoods using a probabilistic model of residential choice; sellers employ an aspiration level heuristic to set their reservation offer price; prices are determined through a continuous double auction. We first provide an approximate analytical solution of the ABM, shedding light on the structure of the model and on the effect of the parameters. We then simulate the ABM and find that: (i) a more unequal income distribution lowers the prices globally, but implies stronger segregation; (ii) a spike of the demand in one part of the city increases the prices all over the city; (iii) subsidies are more efficient than taxes in fostering social mixing.J.E.L. codes: C63, D31, O18, R23, R31 Keywords: Agent-Based Model, Housing Market, Residential Income Segregation, Aspiration Level Heuristic, Urban Economics * Corresponding author: marco.pangallo@maths.ox.ac.uk. We thank seminar participants at WE-HIA 2016, EAEPE 2016, ISCPIF, SCCS 2015, IRES Piemonte and INET Oxford, in particular Henri Berestycki, Allan Davids, J. Doyne Farmer, Michele Loberto, Mauro Napoletano, David Pugh and Matteo Richiardi, for helpful comments. We also thank Florian Artinger and Gerd Gigerenzer for pointing us to their work on aspiration level heuristics, and Marco Ranaldi and R. Maria del Rio Chanona for carefully reading the manuscript. A previous version of this manuscript circulated under the title "Price formation on a housing market and spatial income segregation". This paper reuses material from the master thesis of MP at the University of Torino. MP especially thanks Pietro Terna, who was the main advisor of his thesis, for insightful and frequent discussions.

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Section: Parameter Valuessupporting

confidence: 68%

Residential Income Segregation: A Behavioral Model of the Housing Market

2017

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“…Previous research on Automated Valuation Models for real estate has been initially led by so-called "hedonic" models (Goodman & Thibodeau, 2003;Hu et al, 2013;Lisi & Iacobini, 2013;Loberto et al, 2018). "Hedonic" literally suggests that the buying of the target property, and hence living in it, is a source of "pleasure".…”

Section: Hedonic Modelsmentioning

confidence: 99%

“…For a real estate appraisal company, the goal is to obtain an authoritative valuation for some property. Our target property price is thus corresponding to valuations performed by a registered expert, not to the prices as obtained from Web advertisements, as, e.g., in (Loberto et al, 2018;Moosavi, 2017). We however did use such advertised prices, but only as so-called "comparable" properties (see Section 4 below), that are routinely used by appraisal experts.…”

Section: Target Pricementioning

confidence: 99%

Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources

Bergadano

Bertilone

Paolotti

et al. 2021

INTREST

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In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM described here can help the stakeholders in this process (experts, appraisal company) to provide a reference price to be used by the expert, to allow the appraisal company to validate their evaluations in a faster and cheaper way, to help the expert in listing a set of comparable properties, that need to be included in the appraisal document.

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“…In this respect, Banca d'Italia has a longstanding tradition in assessing the short-term outlook on the basis of this kind of "unconventional" data, starting from forecasting models based on the consumption of electricity (Bodo and Signorini, 1987;Marchetti and Parigi, 2000) and, more recently, on road and rail transport flows. Studies relying on data from the Internet include Google-based models, as in D' Amuri and Marcucci (2017); analyses of the real estate market grounded in a dataset of housing advertisements, as in Pangallo and Loberto (2018) and Loberto, Luciani and Pangallo (2018); and attempts at measuring inflation expectations using Twitter, as in Angelico et al (2018). Finally, more traditional mediasuch as newspapershave been exploited to build news-based sentiment and uncertainty indicators based on text-mining techniques (Aprigliano et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Exploiting Payments to Track Italian Economic Activity: The Experience at Banca D’Italia

et al. 2021

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This paper provides an overview of how information on payments has been recently exploited by Banca d'Italia staff for the purposes of tracking economic activity and forecasting. In particular, the payment data used for this work are drawn from the payment systems managed by Banca d'Italia (BI-COMP and TARGET2) and from the Anti-Money Laundering Aggregate Reports submitted by banks and by Poste Italiane to the Banca d'Italia's Financial Intelligence Unit (Unità di Informazione Finanziaria, UIF). We show that indicators drawn from these sources can improve forecasting accuracy; in particular, those available at a higher frequency have proved crucial to properly assessing the state of the economy during the pandemic. Moreover, these indicators make it possible to assess changes in agents' behaviour, notably with reference to payment habits, and, thanks to their granularity, to delve deeper into the macroeconomic trends, exploring heterogeneity by sector and geography.

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The Potential of Big Housing Data: An Application to the Italian Real-Estate Market

Cited by 23 publications

References 34 publications

Residential Income Segregation: A Behavioral Model of the Housing Market

Residential Income Segregation: A Behavioral Model of the Housing Market

Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources

Exploiting Payments to Track Italian Economic Activity: The Experience at Banca D’Italia

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