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
Outline
• Introduction
• Description of administrative data sources
• Methodology
• Results
• Conclusions and final remarks
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”
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
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)
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)
Administrative data
40000
35000
30000
25000
20000
15000
10000
5000
0
Bank appraisals available for HPI compilation
Dwelling trans. for residential purposes (IMT)
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
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
number of rooms
• Type of dwelling: house or apartment; and
• Occupancy status of dwelling: “new” and “existing”
dwellings
• 56 strata (elementary indexes, geometric mean formula)
Methodology
• Transactions-based index:
– Fiscal administrative data
– Hedonic price index
• Adjacent time dummy approach
Methodology
 q  Q  1, Q;  i  1,..., n(q)
K
ln( P)i ,q  a    k X i ,q ;k  Q Di ,q ;Q   i ,q
K 1
where,
ln( p)i ,q , is the log price of the ith dwelling transaction in quarter q;
X i ,q , stands for the value of the kth characteristic of the ith transacted dwelling in quarter q;
DQ , is the temporal indicator of quarter Q, which is defined as:
1, if q  Q, and
 q  Q  1, Q and  i  1,..., n(q), Di ,q ;Q  
0, if otherwise.
Q , is the parameter associated to the temporal indicator of quarter Q; and
 i ,q , corresponds to an error term.

IQQ1  exp  .100
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
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
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
1,050
60000
55000
50000
1,000
45000
40000
0,950
35000
30000
25000
0,900
20000
15000
0,850
10000
5000
0,800
0
HPI_Strat
HPI_BankA
HPI_Hed
HPI_Ci
N_trans
10,0
5,0
0,0
-5,0
-10,0
-15,0
HPI_Strat
Mean
Stdev
HPI_BanA
HPI_Hed
HPI_Ci
HPI_Raw
HPI_Strat
HPI_BanA
HPI_Hed
HPI_Ci
-3.9%
4.67 p.p.
-3.0%
3.46 p.p.
-3.2%
3.53 p.p.
-1.0%
2.17 p.p.
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
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
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 1Q20112Q2012 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
Results
• Few issues to point out (cont.):
– Number of sales indicator is synchronized with the
behavior shown of appraisals- and transactionsbased HPIs
Conclusions and final remarks
• Results suggest that:
– Asking-based indicators may lag behind transactions- and appraisalsbased 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)