Using administrative data sources to develop real estate price statistics: The case of Portugal

1. Using administrative data sources to develop real estate price statistics: The case of Portugal Rui Evangelista, Statistics Portugal European conference on quality in official statistics Vienna, 4 June 2014

2. Outline • Introduction • Description of administrative data sources • Methodology • Results • Conclusions and final remarks

3. Introduction • Recent economic and financial crisis reinforced the need for more and better statistics on the housing market • Statistical offices started to develop strategies to meet new (…old…) user’s needs: • Eurostat’s statistical pilot program on Owner-occupied Housing (started in 2002) • Statistics Portugal joined the program in 2008: “double data source approach”

4. Introduction • Legal framework: • Regulation No 93/2013: regular provision of the HPI to Eurostat • Regulation No 1176/2011: HPI in the scoreboard of indicators for the early detection of macroeconomic imbalances

5. Administrative data • Two sources: 1 - Bank appraisals • Value of appraised dwellings (mortgage credit processes) • Before any transaction actually takes place • National coverage (almost complete universe of banks conceding mortgage credit) • 326 thousand observations, an average of 16.3 thousand per quarter (1Q2009-4Q2013)

6. Administrative data • Two sources (cont.): 2- Fiscal administrative data • Transaction value: Municipal Tax on Real Estate Transfer (IMT) • Characteristics of the dwellings: Local Property Tax (IMI) • 450 thousand observations, an average of 22.5 thousand per quarter (1Q2009-4Q2013)

7. Administrative data

8. Administrative data • Points to highlight (from the chart): • The number of Bank Appraisals only outscores those of transactions in the first quarters (mortgage credit more abundant) • During the 2Q2009-4Q2012 period, bank appraisals drop considerable (and generally at a faster rate than transactions) • From 4Q2011 onwards, bank appraisals numbers represent less than half the number of transactions

9. Methodology • Appraisals-based HPI: • Compiled using a stratification approach • The strata are defined using the following basic design: • Location of appraised dwelling: as defined by the 7 NUTS II regions for Portugal • Dimension of appraised dwelling: 2 categories based on the numberofrooms • Type of dwelling: house or apartment; and • Occupancy status of dwelling: “new” and “existing” dwellings • 56 strata (elementary indexes, geometric mean formula)

10. Methodology • Transactions-based index: • Fiscal administrative data • Hedonic price index • Adjacent time dummy approach

11. Methodology where,

12. Methodology • The parameters of the hedonic equations are estimated by ordinary least squares (OLS) for the following strata: • existing apartments • existing houses • new apartments • new houses • Special attention was given to location, area and age effects • Robust statistics, tests of individual and joint significance of parameters are used in the specification and estimation process

13. Methodology • House sales indicator • Based on IMT data; restricted to reflect transactions of residential properties only • Agricultural land, commercial and non-arms length transactions (i.e., inherited dwellings) were excluded from the scope of the indicator • As in the transactions-based HPI, transactions of parts of dwellings were excluded from the calculations of the indicator • Results for: apartments/houses and new/existing splits; and by NUTS II region

14. Results • Comparison between: • Bank appraisals HPI: HPI_BankA • “Hedonic” transactions-based HPI: HPI_Hed • “Stratified” transactions-based HPI: HPI_Strat • “Unadjusted” (four basic strata) HPI: HPI_Raw • Asking-price HPI: HPI_Ci • Base 100 = 1Q2009 • Number of house sales: N_trans

17. Results • Few issues to point out: • Despite the drop in bank appraisals counts, appraisals-based HPI seems to mimic its transactions-based counterpart reasonably well: • Same turning point in 2Q2012 • Strong correlation between the two indicators

18. Results • Few issues to point out (cont.): • Asking prices indicator (HPI_Ci) seems to lag behind both appraisals- and transactions-based HPIs: • Contemporary correlation between HPI_Ci and HPI_BankA and HPI_Hed are 0.68 and 0.44, respectively • The figures increase to 0.85 and 0.64 when the HPI_Ci of quarter Q+1 is compared with HPI_BankA and HPI_Hed of quarter Q • HPI_Ci is less volatile, more “resistant” to price drops • Should come as no surprise: representative of prices at the start of the buying and selling process, which tend to be, when the market is depressed, higher than real transaction prices

19. Results • Few issues to point out (cont.): • Difference between HPI_Strat and HPI_Hed is bigger when the market hits its lowest point • Stratification approach seems not to fully account the change in the quality mix of transacted dwellings • A stratification scheme with less strata (only 4; HPI_Raw) shows even sharper price decreases in the 1Q2011-2Q2012 period • Results suggests that at least part of the price decreases shown by the HPI_Strat indicator should be (at least partly) attributed to the fact that cheaper and worse quality dwellings are driving average prices down

20. Results • Few issues to point out (cont.): • Number of sales indicator is synchronized with the behavior shown of appraisals- and transactions-based HPIs

21. Conclusions and final remarks • Results suggest that: • Asking-based indicators may lag behind transactions- and appraisals-based HPIs • Bank appraisals may be a reasonable source to develop a HPI (“second-best approach”; need for more research) • Overall, it is possible to develop good-quality real estate statistics based on administrative data sources • In the case of Portugal, a change from bank appraisals to fiscal administrative data would represent a jump in the quality of provided official statistics: • Methodological soundness : e.g., use of transaction values (instead of a proxy) • Accuracy and reliability: use of more appropriate methods to tackle quality change (“pure” price change would be better measured)