=Paper=
{{Paper
|id=Vol-1963/paper561
|storemode=property
|title=Publishing Socio-Economic Territory Indices as Linked Data and their Visualization
for Real Estate Valuation
|pdfUrl=https://ceur-ws.org/Vol-1963/paper561.pdf
|volume=Vol-1963
|authors=Dina Sukhobok,Divna Djordjevic,Diego Sanvito,Javier Paniagua,Dumitru Roman
|dblpUrl=https://dblp.org/rec/conf/semweb/SukhobokDSPR17
}}
==Publishing Socio-Economic Territory Indices as Linked Data and their Visualization
for Real Estate Valuation==
https://ceur-ws.org/Vol-1963/paper561.pdf
Publishing Socio-Economic Territory Indices as
Linked Data and their Visualization for Real
Estate Valuation
Dina Sukhobok1 , Divna Djordjevic2 , Diego Sanvito2 , Javier Paniagua3 and
Dumitru Roman1
1
SINTEF, Forskningsveien 1a, 0373 Oslo, Norway
{dina.sukhobok,dumitru.roman}@sintef.no
2
CERVED, Via della Unione Europea, 6/A-6/B, 20097 San Donato Milanese, MI,
Italy {divna.djordjevic,diego.sanvito}@cerved.com
3
SpazioDati S.r.l., Via A. Olivetti 13, 38122, Trento (TN), Italy
paniagua@spaziodati.eu
Abstract. The correct estimation of the real estate value facilitates
decision making in various sectors, such as public administration or the
real estate market. In this paper we demonstrate a method to manage
territory scores and property valuation estimations as Linked Data with
the help of the proDataMarket technical framework. The demo illustrates
how the proDataMarket technical framework can be used to generate,
maintain and serve territory and property valuation estimation data with
the help of semantic technologies.
Keywords: Linked Data, socio-economic indices, property data, real
estate data
1 Introduction
The correct estimation of the real estate value of residential properties owned by
companies and individuals is one of the crucial elements for understanding their
economic behavior and for predicting their financial stability. In order to improve
current real estate property evaluation process, Cerved (an Italian data-driven
company and a credit rating agency, providing services as credit information,
marketing and credit management)4 introduced a new algorithm for the evalu-
ation of residential property that uses property data, open data and third-party
data. The algorithm was implemented in the Cerved Cadastral Report Service
(CCRS) enabling automatic estimate of the current market price for Italian res-
idential properties following the Automated Value Model (AVM) methodology
5
. The final outcome of the service is a set of property valuation scores for
the entire Italy, at the level of census sections from the 15th population and
4
http://www.cerved.com/en
5
https://en.wikipedia.org/wiki/Automated_valuation_model
housing census by ISTAT6 . The approach estimates current properties values
by taking into consideration values of comparable properties, number of rooms,
property conditions, indication of value by revenue agency, comparable sales
analysis of similar properties, surrounding sociodemographic and economic phe-
nomena indicators such as schooling, pollution, type of industry, traffic, health
care facilities, type of employment, revenue estimate, etc. In addition, higher
level territory estimations have been created to analyze the marketing potential
of a specific territory. The calculated property values and territory scores are
then made available as Linked Data. The targeted customer segment is repre-
sented by Italian banks using the property valuation service for mortgage issuing
or mortgage portfolio revaluation. The main goal of the service is to provide an
accurate and objective evaluation of the real estate properties, contextualized to
the market and to the territory of belonging, and updated in real-time. Cerved
has also developed the Cerved Scouting the Terrain (CST) service, a Web-based
map application that supports visualization and data aggregation of territory
scores and property valuations from CCRS. The application was developed for
Cerved’s internal property appraisal department for increasing efficiently and
quality of service when providing a range of products by exploring a selected
area and a comparable set of properties.
In addition, all the developed territory scores and property valuation estima-
tion data have been integrated, analyzed and can be easily visualized through a
technology framework developed as part of the proDataMarket project7 .
2 Approach and Implementation
Data Sources. The estimated values of the properties derived by the CCRS
algorithm are based on property data, numerous land-based socio-demographic
scores, historical data on real estate appraisal from Cerved’s proprietary database,
property market values aggregated by the Italian revenue agency (i.e. OMI
zones database), etc. The data used for the CCRS include various open, propri-
etary and 3rd party datasets. Including cadastral database, OMI zone database,
company database including types of industry, and managers and shareholders
database. Open data includes ISTAT data from the 2011 census, and Open-
StreetMap (OSM) database8 . The ISTAT data consists of tabular and shape
data covering the Italian census from 2011 and contains data for 366,000 census
sections with residents. These sections, in general, correspond to one district or
a part of it, and are used as territory bases for the various developed scores. The
disaggregated scores for the census sections include:
– Social demographic score: A score developed by Cerved using numerous
socio-demographic variables from the ISTAT national census of 2011 and
validated with the proprietary property appraisal dataset.
6
http://www.istat.it/en
7
https://prodatamarket.eu/
8
http://wiki.openstreetmap.org/wiki/Database
– Index of Social Distress (IDS): A score defined by the decree of the president
of the ministers council from 20159 , based on employment, unemployment,
juvenile concentration and education rates from the ISTAT national census
of 2011.
– Index of Economic Distress (IDE): A score from the above referenced de-
cree and variables from the 2011 census, based on proportion of residential
properties in the urban areas in bad and medium state of preservation.
– Manager and ShareHolders Concentration (MSHC) score: A score based on
Cerved official and proprietary data regarding people in roles of managers
and shareholders.
– Heavy Industrial Concentration (HIC) score: A score from Cerved’s official
and proprietary data on industries in certain NACE10 categories.
– Higher level integration scores: People score integrating the MSHC and the
social demographic score, territory score integrating the HIC score and vari-
ous proprietary features of the territory (e.g., OSM dataset), and the overall
real estate integrated score.
The calculated scores are provisioned in a tabular format.
Ontology description. On a semantic level, we used the proDataMarket
ontology[5] for describing census cells geospatial attributes, socio-demographic
data from the ISTAT national census of 2011 and property valuation estima-
tion scores. Census cells were defined as geospatial objects to capture geospatial
attributes of a census tract, whereas all the scores and socio-demographic data
were described with the help of the generic concept of Indicator from the pro-
DataMarket Common Vocabulary11 .
Linked Data Generation and Publication. Linked data generation and
data publication was performed using the proDataMarket platform – a part of
the proDataMarket technical framework, used for data cleaning, data transfor-
mation and data hosting. The proDataMarket platform includes a set of software
components - DataGraft12 [2,3] and Grafterizer[4] and facilitates interactive spec-
ification of tabular data transformations and mapping of tabular data to graph
data (RDF) and publishing data as a SPARQL endpoint.
3 Demonstration Outline
During the demo we will introduce the proDataMarket platform and show how
it can be used to generate Linked Data from tabular data and publish it through
a SPARQL endpoint, using property valuation scores as an example.
9
http://www.gazzettaufficiale.it/eli/id/2015/10/26/15A08012/sg
10
http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:
Statistical_classification_of_economic_activities_in_the_European_
Community_(NACE)
11
http://vocabs.datagraft.net/proDataMarket/0.1/Common
12
https://datagraft.io/
In addition, we will present the visualization of various indices in the pro-
DataMarket portal and through CST Web-based map application (see Figure 1),
by using data from the SPARQL endpoint and displaying several property val-
uation estimation scores based on census sections and properties details. In the
proDataMarket portal, users have the ability to navigate the territory map, with
the possibility to zoom-in and zoom-out, combine filters to fulfill more specific
queries, select and visualize on map different data layers for different property
datasets and to analyze the territory (through a dedicated colour scale for each
layer of data).
Fig. 1. Property valuation estimation scores in the CST application.
Acknowledgements This work is partly funded by the EC H2020 project proData-
Market (Grant number: 644497).
References
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10.14609/Ti 2 16 2e, Pages 27-40, ISSN 2499-2674.
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(Satellite Events) 2016: 101-106.
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