Data from RE distressed market: Properties auctions in Italy

This paper reports data describing Real Estate (RE) distressed market, focusing on properties foreclosures occurred in North-East Italy. A survey was carried out consulting financial institutions, courts of law and different associations of public notaries. The aim of this survey was to record RE auctions, collecting technical and socio-economic features. The novelties of this survey are mainly two. The first consists in the dataset itself, due to the difficult in collecting such type of data in the Italian scenario. The second one is the recording of socio-economic features related to the occurrence of the survived Re auction. The collected socio-economic characteristics regard housing market trends and performance as well as demographic features. These features could be analyzed in order to relate the performances of this type of distressed market and the surrounding urban context. The database come from an analysis of the authors regarding the discount existing between the Forced Sale Price and the Market value, assessed by appraisers.


a b s t r a c t
This paper reports data describing Real Estate (RE) distressed market, focusing on properties foreclosures occurred in North-East Italy. A survey was carried out consulting financial institutions, courts of law and different associations of public notaries. The aim of this survey was to record RE auctions, collecting technical and socio-economic features. The novelties of this survey are mainly two. The first consists in the dataset itself, due to the difficult in collecting such type of data in the Italian scenario. The second one is the recording of socio-economic features related to the occurrence of the survived Re auction. The collected socio-economic characteristics regard housing market trends and performance as well as demographic features. These features could be analyzed in order to relate the performances of this type of distressed market and the surrounding urban context. The database come from an analysis of the authors regarding the discount existing between the Forced Sale Price and the Market value, assessed by appraisers. &

Value of the data
The presented data is the first to be published on the Italian auction market. There are no other public databases that describe this distressed market.
The records describe auctioned properties surveying both technical and socio-economic features, affecting and/or effected by the auction performance.
The collected data are easy to interpret and can be processed by qualitative and quantitative statistical analysis, e.g., rough set analysis and hedonic regression models.
Demographic data and RE auction performance are demonstrated to be related by international [1], but no evidence is still carried out in the Italian context.

Data
This database denotes the main novelty of the paper. In the Italian milieu, all data on RE auction market and foreclosure procedures are collected by associations of public notaries and courts of law in hardcopy archive. Therefore, this survey sheds light on the Italian RE auction market, on its procedure and on the context features, in which it takes place [2].
We collected data on RE auction procedures by cooperating with associations of public notaries in the Veneto Region. Our database is composed by 125 forced sale properties in the North-East Italy, auctioned between 2008 and 2016. Table 1 lists the selected surveyed characteristics, selected both by consulting literature [3][4][5][6][7] and according to the aims of our survey. All the features are clustered into four different groups (Physical Features-PhF, Profitability Features-PF, Socio-Economic Features-SEF, Auction Market Features-AMF), classified by an Identification Code (ID), and identified by a Coding System.

Experimental design, materials and methods
We restricted the survey both limiting the temporal extension and the geographical dispersion, thus ensuring some degree of homogeneity [8][9][10][11][12]. Therefore, we focused the sampling in a restricted  area, in order to avoid several variables relating to purely territorial dynamics [13][14][15]. We also surveyed data for an eight years period, after the big financial collapse in 2016.
For each auctioned property we surveyed the information listed in Table 2 by consulting the archives of the above-mentioned institutions. We collected data on physical characteristics of the properties (such as typology classification, legal description, size, pictures, quality of constructions and state of maintenance), but also data connected to the distressed market where they were sold (such as fair market value, date of sale, date of value, estimated value, methodological approach and number of bidding proceedings).
We filled out all the survey charts, consulting local paper archives. Successively, we listed our database first summarizing the surveyed features and subsequently analyzing and processing some listed characteristics in new interpretative variables, clustered in SEF and AMF groups. The PhF, PF and AMF clusters consist of characteristics, which are chosen consulting international literature [2,4,16]. Instead, the features in SEF cluster are selected by the authors in order to capture and interpret socioeconomic condition of the surrounding area, where the auction takes place [17][18][19].
Furthermore, we cluster the data in the following groups: Occupancy of the auctioned property (Oc). This variable was selected in order to considered whether the house generates income. Indeed, Clauretie and Daneshvary [20] showed that the occupancy status influences foreclosure discounts. We survived if it is vacant (0), rented to tenants (1), or occupied permanently or occasionally by the former owner (2). 3) Socio-Economic Features (SEF): These variables represent, on the one hand, the property's physical location in relation to the city sprawl and, on the other hand, the socio-economic features that characterize the local market. These variables were selected to be able to interpret the activity of the involved market, in fact a Chow et al.'s study [21] explain that strong competitive market demand (when the number of bidders and transactions is large) leads also to higher forced sale prices. These variables could be useful in examining possible relationships existing between the performance of the competitive RE market and the high discounts (collected in AMF cluster) that effects the RE auction market.  (Table 2), in order to describe the performance of the Italian RE auction market: -Days on the market (DD): the number of days a property stays on the auctioned market before being sold. This variable is calculated as the difference between the date of sale (closing date) and the first auction starting date (opening bid). -Numbers of auctions (NA): the number of bidding proceedings before the selling.
-Discount (Ds): this variable represents the percentage variation between the first listing value (which equal to the market value assessed by the appraisal) and the selling price, which the property is sold. It is important to stress that, to compare the values over time, all the selling price were discounted before the calculation of the Ds, in order to avoid temporal bias. We estimated a Real Estate price index, consulting the RE quotations archives published by Consulente Immobiliare [22], to discount the selling prices. We developed a matrix for each surveyed province that covers the entire period from 2008 until 2016. -Premium (Pr): this variable represents the premium paid by the winner bidder to win the auction. It is calculated as the percentage variation between the last listing value and the final selling price, which the property is sold. As for the Ds we discounted the selling price consulting the indexes matrix described above.