1. Workshop on Integrating Geospatial and Statistical Standards
Session 3: Challenges and Solutions for Creating Geospatial Statistical Outputs
Location analytics in administrative data to produce House Price Statistics in Portugal
FRANCISCO VALA
INÊS FONTES | ELSA SOARES | FRANCISCO CALDEIRA
Stockholm, 6-8 November 2017

2. AGENDA
Motivation for disseminating House Prices Statistics at Local Level 1
Administrative data sources in use and options defined for location analytics 2 3
Indicators and products disseminated 4
Lessons learnt and critical issues

3. Motivation
Among the statistical gaps highlighted by the financial and economic crisis, real estate price statistics have been considered one of the areas to be urgently developed to provide appropriate indicators for both residential and commercial property prices.
Statistics Portugal is already addressing these two topics of real estate prices by taking advantage of administrative data:
House Price Index (HPI) is released quarterly since July 2014 (EC Regulation 93/2013)
Commercial Property Price Index (CPPI) is released annually since June 2017
Burden reduction and simplification is also at the heart of the modernisation programme that is the ESS Vision 2020, which aims to replace traditional surveys with new data sources, in particular administrative ones, to exploit synergies and to implement more modern and efficient production methods

4. Motivation within EU countries (e.g. capital cities, touristic areas)
However, there are significant regional differences in house price developments due to differentiating territorial assets:
within EU countries (e.g. capital cities, touristic areas)
within regions (e.g. city centres, coastal areas)
House prices statistics at local level aims to provide new quarterly official information to:
monitor territorial differentiation of house prices dynamics
explore house prices within cities based on a Web GIS application
Burden reduction and simplification is also at the heart of the modernisation programme that is the ESS Vision 2020, which aims to replace traditional surveys with new data sources, in particular administrative ones, to exploit synergies and to implement more modern and efficient production methods

5. Administrative sources
Statistics on house prices at local level are based on tax data provided by the Portuguese Tax and Customs Authority (AT) → agreement signed with Statistics Portugal (same framework as for HPI and CPPI)
The project is based on data linkage between information from:
Municipal Property Transfer Tax: from which the transaction prices are obtained → flow data
Municipal Property Tax: from which identifying characteristics of the transacted dwelling are obtained → stock data
The link between these two sources is made at the dwelling level
Burden reduction and simplification is also at the heart of the modernisation programme that is the ESS Vision 2020, which aims to replace traditional surveys with new data sources, in particular administrative ones, to exploit synergies and to implement more modern and efficient production methods

6. Administrative sources
Municipal Property Transfer Tax (IMT) Transaction prices
STATISTICS PORTUGAL SDI
BGRI Blocks (polygons)
BGE Buildings (points)
FNA Household register (address)
Energy Certification
(…)
X,Y
Municipal Property Tax (IMI)
Characteristics of the dwelling
(…)
LAU
X,Y
Address
Administrative boundaries
Statistical City
Statistical GRID 500 x 500 m
Statistical section (BGRI)

7. Validation for location analytics
X,Y Domain rules
With lack of information: 6.6%
Incorrect geographic coordinates (different reference systems and other errors): 0.01%
X,Y Consistency rules: Local Administrative Units (LAU)
LAU2 different but LAU1 equal: 1.11% → keep location based on X,Y
LAU1 different: 0.07% → keep location based on LAU (refuse X,Y)
Data from
With XY transactions
9696 different LAU2 and equal LAU1
635 different LAU1
LAU / City
Complete transactions coverage
Imputation
GRID and Statistical section
Completeness above 90%

8. Dissemination for location analytics
Average prices per square meter vs Median prices per square meter
While average prices are more common, median prices allow to remove the effect of extreme prices which may be particularly relevant at local level statistics.
Reference period for quarterly dissemination
Although the results are interpreted on a quarterly basis, they reflect the sales within a 12-month period, which reduces the impact of irregularities associated with the heterogeneity of the dwellings sold and eliminates the effect of potential seasonal fluctuations.
Dissemination thresholds
For each territorial unit, a minimum number of 15 transactions is considered for the four quarters ending in the reference quarter. In the case of data by subsection and grid the minimum number is 7.
At LAU1 level there are still 5 municipalities bellow the dissemination threshold

9. Indicators and products
Indicators available at Statistics Portugal Official Website
The quarterly results for the period between the first quarter of 2016 and the second quarter of 2017 are available at
For the country, for the three NUTS levels and also for the municipalities.
Median value per m2 of dwellings sales (€) by Geographic localization (NUTS ); Quarterly
For the country and up to the NUTS 3 level regions. In the case of Metropolitan Areas (Lisboa, Porto) and Algarve the values by LAU1 and 2 are also released.
Median value per m2 of dwellings sales (€) by Geographic localization and Category of housing unit; Quarterly
Median value per m2 of dwellings sales in flats (€) by Geographic localization; Quarterly
Median value per m2 of dwellings sales in existing flats (€) by Geographic localization; Quarterly
For the 7 cities with more than 100 thousand inhabitants (Lisboa, Porto, Vila Nova de Gaia, Amadora, Braga, Funchal and Coimbra). For Lisboa and Porto, data is also released by LAU 2.

10. Indicators and products
Press release
31 October 2017

11. Indicators and products
41 out of the 308 Portuguese municipalities scored house prices above national value
Median value per m² of dwellings sales, Portugal, NUTS III and municipality

12. Indicators and products Category of housing unit
The price of existing dwellings in Lisboa (2 146 €/m2) surpassed the prices of new dwellings in the remaining municipalities of Área Metropolitana de Lisboa
Median value per m² of dwellings sales by category of housing unit of Área Metropolitana de Lisboa, by municipality
Category of housing unit
Total

13. Indicators and products
The parishes of Santo António (3 294 €/m2) and Misericórdia (3 244 €/m2) scored the highest dwellings prices and the highest year-on-year rates of change in the city of Lisboa
Median value and year-on-year rate of change of median value per m² of dwellings sales, Lisboa and parishes
Median value per m² of dwellings sales, Lisboa and parishes

14. Indicators and products
Web application
allows the interactive search of median price on dwellings sales (€/m2) for the cities of Lisboa and Porto, for territorial units defined by the user based on the statistical section or a 500m x 500m grid, facilitating the analysis of selling prices charged in the different areas of each one of the cities.

15. Indicators and products
Web application
allows the interactive search of median price on dwellings sales (€/m2) for the cities of Lisboa and Porto, for territorial units defined by the user based on the statistical section or a 500m x 500m grid, facilitating the analysis of selling prices charged in the different areas of each one of the cities.

16. Impact: social media and news

17. Lessons learnt and critical issues
Territorial statistics and spatial data allows for new dimensions of analysis and insights, that tend to be relevant for business, public policies and the general public.
Institutional arrangements and technical cooperation?
Maintenance of quality standards of (external) input data and data availability?
Share infrastructural (geospatial) data and agree on a single system of codification across Public Administration?
Stand for differentiated ‘fit for purpose’ quality assurance or a parallel set of experimental statistics?
Burden reduction and simplification is also at the heart of the modernisation programme that is the ESS Vision 2020, which aims to replace traditional surveys with new data sources, in particular administrative ones, to exploit synergies and to implement more modern and efficient production methods

18. Workshop on Integrating Geospatial and Statistical Standards
Session 3: Challenges and Solutions for Creating Geospatial Statistical Outputs
Location analytics in administrative data to produce House Price Statistics in Portugal
FRANCISCO VALA
INÊS FONTES | ELSA SOARES | FRANCISCO CALDEIRA
Stockholm, 6-8 November 2017