On the derivation of hedonic rental price indexes for commercial properties: International ...
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On the derivation of hedonic rental price indexes for
commercial properties:
Rent price determinants, data quality problems and evidence from the use
of administrative data for the Portuguese market1◊
Evangelista, R., Moreira, H., and Teixeira, Â.
Abstract:
Although important to provide a full picture of commercial property markets, examples
illustrating the compilation of price indexes covering the market for commercial space remain
scarce in the literature. The purpose of this paper is to help closing this gap by presenting the
first results of the application of the hedonic imputation method to a unique and rich data set
covering information of more than 146 thousand retail, service and industrial rent agreements
carried out from 2015 to 2017 in Portugal. The paper presents a comprehensive literature
review on this topic and the work done to overcome data shortcomings such as the absence of
variables and partly missing observations. Preliminary results suggest that it is possible to
derive useful information on the evolution of commercial rents, even when important variables,
such as the length of the rental contract, present quality issues. This work is part of Statistics
Portugal’s efforts to provide new and improved statistics covering the real estate market, which
resulted in the 2017 release of a Commercial Property Price Index and prompted the
submission of a grant proposal for the development of commercial property price statistics until
2020.
Keywords: Commercial real estate, rental price index, hedonic price model, missing data
◊
The views expressed here are those of the authors and should not be attributed to Statistics Portugal or to any other
institution mentioned in this paper.
1. Introduction
The commercial property market is central not only to institutional investors, who compose
their portfolios by investing in commercial real estate assets, but also for enterprises, self-
employed people and individuals who see the rental of an office or retail unit as a cost factor or
a source of revenue. In this context, the existence of good quality price indicators is of
paramount importance to enhance market transparency and efficiency. These indicators are also
needed as inputs in national accounts (Diewert et al., 2016) and in the compilation of official
statistics, such as for price indices for services of real estate activities.2 While there has been
some progress towards the increase of the offer of commercial property price indexes,
information on the rental of commercial property space remains largely unavailable. Portugal is
no exception to this situation, where there are no official figures for the rental market of
commercial properties.
The present paper presents the preliminary results of commercial property rent indexes
(CPRIs) using the hedonic price function (Rosen, 1974) for January 2015 - December 2017. It
continues the efforts of Statistics Portugal in developing new price indicators for the real estate
market, in which the release of a commercial property price index (CPPI) is an example
(Raposo and Evangelista, 2017). This exploratory work is carried out under the scope of a two-
year grant agreement project signed between Statistics Portugal and Eurostat, aiming at
assessing the possibility of producing indexes for the rental of commercial space until 2020.
The work provided here is based on a dataset with information of more than 146 thousand
rental contracts containing around 2.8 million rent receipts, one of the largest used to derive
CPRIs. The dataset is the result of the combination of administrative records taken from the
Portuguese Tax and Customs Authority (AT),3 the National Energy Agency (ADENE)4, Census
and other information. Notwithstanding its dimension, the available dataset presents some
limitations in terms of coverage and quality. By exploring its strengths and weaknesses, this
paper contributes to all those interested in the construction of a CPRI and in having a more
complete knowledge of the behaviour of commercial property rentals in Portugal.
This paper is organized as follows. Section 2 reviews the literature associated with
commercial property rent determinants and indexes. Section 3 presents the framework in which
the results of the indexes are going to be derived. Section 4 describes the data sources and data
used in this study. Section 5 provides the empirical results of the hedonic indexes. Finally,
section 6 provides a summary of the main findings and points out the directions of future work.
2
Although the reporting of this producer price index is not presently demanded by Eurostat, its compilation is recommended in
international manuals (e.g., OECD, 2014).
3
The Portuguese Tax and Customs Authority, or Autoridade Tributária e Aduaneira, is often referred in its abbreviated form, which
will be used in the text whenever there is a need to mention it.
4
This agency is known by ADENE. This acronym will be used throughout this text whenever it is necessary to refer it.
12. Literature review
The literature associated with the use of the hedonic price model for commercial property
rents can be broadly divided into two areas of research. The first one focuses on the importance
of a particular (or a specific group of) characteristic(s) in the formation of rent levels (see, inter
alia, Fuerst, 2008). The second research area focuses on the measurement of rental
developments and addresses the building of rental indexes in a direct way; see, for instance
Kempf (2016). The two strands of literature are interrelated in the sense that the hedonic model
specification, which serves the basis for the compilation of hedonic rent indices, needs to
identify and cover rent determining characteristics.
2.1. Determinants of commercial property rent levels
The papers by Clapp (1980) and Frew and Jud (1988) are two illustrations of the earlier
literature investigating the effects of particular commercial property characteristics on rents.
While the former study concentrates on impact of intra-metropolitan office locations on office
rents in Los Angeles, the latter made an interesting contribution to hedonic rent equations by
including vacancy rate as an explanatory variable. For the industrial and commercial (shopping
centres) markets, Ambrose (1990) and Sirmans and Guidry (1993) constitute two examples of
this earlier literature. By and large, the scope of this earlier body of literature was limited by the
use of small samples and several data problems (e.g., lack of variables and absence of data on
individual rentals).
Ozus (2009) and Farooq et al. (2010) are more recent examples for the office markets,
respectively. Sherry (2015), who focuses on the importance of the access to light rail transit and
highway systems in office and industrial property rents, and Nase and Adair (2013), who
investigate the effect of design quality on the rents for high street retail properties, are examples
for the retail and industrial markets.
In general, rent determinants can be grouped into four main groups. The first one refers to
location attributes and are useful to model the spatial relationships associated with commercial
property rents. Examples of these factors range from the accessibility to modes of transport, to
the quality of the surroundings in which the commercial property market is located. The second
group of determinants refers to the physical characteristics of the commercial property. The
construction quality of the building in which the commercial property is located, its age and
area are examples of these variables. Environmental features, such as those associated with the
existence of green properties, can also be included in this group of characteristics. Eichholtz et
al. (2013) is an example of a study that looks for qualitative evidence supporting the idea that
green office buildings display a rent (or price) premium when compared to conventional office
2space. The third group of attributes is related to the characteristics of lease contract agreements.
This group includes not only tenants and landlords’ characteristics (e.g., their type and
creditworthiness), but also contractual agreement features such as its length. In relation to this
last attribute, which is identified as an important rent-determining factor and is often not
available in datasets 5, the empirical evidence on its impact is mixed. In theory, it can be argued
that, due to the existence of transaction costs (of a change of tenant) and other factors, landlords
prefer longer leases over shorter ones. Following this reasoning, longer leases should receive a
price discount. However, especially in bullish markets, it can also be argued that the shape of
the term structure is upward sloping as longer leases would prevent the setting of increasingly
higher rental prices in short-term leases. In practice, this means that the relationship can be
positive, negative or even statistically insignificant. Finally, the last group of characteristics
refers to the impact of the state of the market (e.g., unemployment rate) on the determination of
rent levels. With the exception of this last category, the database used in the compilation of the
rent indices covers all groups of characteristics.
2.2. Commercial property rent indexes
The literature revolving around the compilation of hedonic rent indexes is not abundant,
with most of the available studies focusing on the office property market. Wheaton and
Torto (1994) and Webb and Fisher (1996) provide two of the earliest examples of hedonic rent
indexes compiled using information from individual lease agreements of properties located in
several metropolitan areas of the United States. Both studies provide annual indexes based on
the time dummy hedonic method, with the first paper covering the years from 1979 to 1991 and
the second one the 1985 – 1991 period. Slade (2000) constructs a quarterly office rent index for
the Phoenix metropolitan area for the period spanning from the first quarter of 1991 to the third
quarter of 1996. The index is based on asking and other lease information taken from a survey
carried out by a real estate company and is constructed following the adjacent time dummy
hedonic method. Englung et al. (2008) exhibit a rent index series for the Stockholm office
market. Their index is compiled for the 1997-2002 period and is based on the hedonic time
dummy method. Finally, a more recent example is given by Kempf (2016), who provides
indexes for 1997 to 2006 for the Berlin, Dusseldorf, Frankfurt, Hamburg and Munich office
markets. This author provides results based on the time dummy and characteristics hedonic
price index method, with the former approach being preferred over the latter.
Partly due to the lack of data, there is even far less evidence on rent indexes covering other
market segments. Deschermeier et al. (2015) compute half-yearly rental indexes that include, in
5
Some authors claim that offer prices can be used to circumvent the limitation of not having information on the length of
contractual agreements. However, the use of offer prices may not be seen as desired in the construction of a rent index since they are
essentially what the landlord would like to receive from a contract and may, for this reason, give the wrong signal of actual rent
developments (see, for instance, Eurostat, 2017: 100).
3addition to offices, the retail market segment for Berlin. Based on advertised information
gathered from an internet platform, the paper provides a comparison of results using the time
dummy and hedonic imputation methods for a time period ranging from 2008 to 2013. Clark
and Pennington-Cross (2016) show a constant-quality price index for industrial property rents in
the Chicago metropolitan area for 2003 to 2012. The overall index provided in this study
encompasses all type of industrial properties (retail, office, warehouse, etc) and is extracted
from the time dummy variables included in the specification. Finally, An et al. (2015) provides
an interesting example, where a quarterly real estate rental index, covering the retail, office and
industrial properties is derived for the 2001 – 2010 period. Although the index follows the
hedonic framework, it is derived in a non-standard way as it focuses on individual property’s
growth effects instead of focusing on individual attributes. Indices are taken from Generalized
Least Squares (GLS) and Generalized Method of Moments (GMM) estimators.
Table 1 provides a summary of a selected group of features of the studies that provide
commercial property rent index results.
Table 1: Characteristics of hedonic commercial property rent index studies
Paper Market segment Time Sample sizes Hedonic Comments
period method
Wheaton and Office market; 36 236 to 6,800 obs. Adj. R2 square
Time dummy
Torto (1994) metropolitan areas 1979-1991 per metropolitan reported to vary from
method.
(USA) area. .37 to .61.
Webb and Office market;
Fisher (1996) Chicago’s central Time dummy
1985-1991 226 obs. Adj. R2 of .44.
business district method.
(USA)
Slade (2000) Office market; Adjacent time
1Q1993 – Approx. 480 obs. Adj. R2 from 0.25 to
Phoenix metropolitan dummy
3Q1996 per quarter 0.32.
area (USA) method.
Englung et Adj. R2 of .96. Model
Office market; Time dummy
al. (2008) 1972 - 2002 2,485 obs. includes 182 property
Stockholm (Sweden) method.
dummies.
An et al. (2010) All commercial 1Q2001 – GLS and GMM
60,042 obs. -
property types (USA) 2Q2010 estimators.
Deschermeier 17,699 obs. Adj. R2 from.22 to
Office and retail Time dummy
et al. (2015) 1S2008 – (office) and .40. Imputation
market; Berlin and imputation
2S2013 10,862 obs. method reported to
(Germany) methods.
(retail). perform better.
Kempf (2016) Time dummy Adj. R2 from .12 to
Office market; Berlin,
and .43. Characteristic
Dusseldorf, Frankfurt, 1997-2006 22,005 obs.
characteristic outperforms the time
Hamburg and Munich
methods. dummy.
Clark and Chicago’s commercial
Time dummy
Pennington- market; Chicago 2003-2012 2,645 obs. Adj. R2 of .59.
method.
Cross (2016) (USA)
Note: Q – quarter; S – semester.
The majority of the studies are focused on some specific area or particular market segment.
Moreover, the design of the empirical exercises is dominated by applications of the OLS
estimator on cross sectional data. With the exception of An et al. (2010), the sample sizes of the
4studies are not exceptionally large. Data issues, e.g. measurement errors and missing data, seem
not to be particularly covered. An exception is Kempf (2016), who reports high percentages of
missing information for some variables (e.g., over than 50 percent to the number of storeys and
of up to 25 percent for building age). To overcome this problem, the author applies the multiple
imputation method, first introduced by Rubin (1977).
With such a scarce number of empirical studies in the literature available on this topic, it
should come as no surprise that the supply of official commercial property rent indexes by
national statistical institutes is nearly inexistent. Statistics Norway (n.d.) is a notable exception,
where an annual rents index for commercial properties is produced using tax authorities’
administrative data. The index is stratified by type of property, which is defined according to its
declared main use (e.g., shop/shopping mall, warehouse, hotel), and is compiled as quality
adjusted unit values (Chessa, 2016). One of main drawbacks of this index is the fact that it is
released with a considerable time lag (i.e., data are provided eleven months after the end of the
reference year).
There are no international guidelines on how to derive commercial property rental indexes.
Although focused on RPPIs, Eurostat (2017: 99-103) provides a brief overview of the data
sources and methods available for the construction of rental commercial property indexes.
3. Rent index construction framework
The hedonic rent indexes compiled for the purposes of this work follow the imputation
method; for an explanation of this and other hedonic methods used in the compilation of
property price indexes, see Silver (2018).
The construction of this index was done in the same fashion as any other price index, with
shadow prices (i.e., the coefficients taken from hedonic regressions) used in counterfactual
estimation of the rent values that were available in period t-1 and had no comparable rent in
period t, and vice-versa.6 For the contracts that had rent information in both periods, no
imputation was done. This estimation and calculation process was carried out successively for
each pair of contiguous months. The final indexes were obtained by chaining the results for each
pair of months. To test the consistency of the results stemming from the imputation approach,
rent indexes using the time dummy method were also produced.
6
This variant of the imputation method is described in Linz et al. (2009).
54. Data
This section provides an overview of the construction process of the dataset and, in
addition, explores the information contained in it. Although the combination of different sources
resulted in a unique dataset that includes not only rent information, but also variables normally
not available in similar studies (e.g., energy efficiency ratings), its coverage is not complete and
some variables present quality issues, particularly the existence of missing values in the
information of some key potential price determining variables (e.g., the length of the rental
contract).
4.1. Sources, data matching process and restrictions
The data used in the construction of the commercial rent index is taken from AT and
ADENE’s records, which is provided to Statistics Portugal on the basis of transfer agreements.
Information on some administrative location variables (e.g., municipalities or parishes codes)
and other territorial units created in the context of the last (i.e. 2011) Census exercise, were also
taken into account in the dataset.
The data provided by AT is taken from four different data flows. The first one refers to
electronic rent receipts data, which is sent to INE on a weekly basis. As of 31st March 2015,
landlords are obliged to issue rent receipts electronically though the portal of the Ministry of
Finance (Portaria n.º 98-A/2015). This legal obligation extends to all rents issued on paper from
January 2015 onwards, whose information had to be sent electronically with May 2015 receipt
information. Excluded from this data source are the properties whose owners are not by
individuals or sole proprietorship enterprises. The real estate owned by more complex forms of
businesses are not covered by available data, a fact should always be kept present when
analysing the results of rent indexes (see more on this in the next section). The second data flow
pertains to lease agreement information (“Model 2”), where variables such as the duration of the
lease or whether a contract is new can be retrieved. The third data flow refers to information
7
taken from the Local property tax (IMI) data. This source of information provides the
characteristics of each property unit, such as whether it is located in a shopping centre or in an
area with high commercial value. The lease agreements and IMI data are sent monthly to INE.
Finally, the fourth tax data flow is taken from the annual declaration of income from rentals
(“Model 44”).
The IMI data, combined with information on the Municipal transfer tax (IMT), are currently
employed in the compilation of the residential and commercial property price indexes for
Portugal (INE, 2017a; 2017b). A subset of the data available in the IMT and IMI records was
7
The local property tax is designated as Imposto Municipal sobre Imóveis or simply as IMI. This name will be used in the text
whenever there is a need to identify it.
6also used in an empirical application to produce hedonic price indexes (Ramalho et al., 2017).
Rent receipts data are already in use in the compilation of the rents component of the Consumer
Price Index (Mendonça and Evangelista, 2018).
In parallel, ADENE also provides information on the energy performance of properties on a
monthly basis. According to the Energy Performance Certification (EPC) system that was
adopted in Portugal, the energy performance of a property can be presently expressed in an
eight-level scale, which ranges from A+, the most efficient level, to F, the least efficient level.8
Energy certification has a mandatory status for all advertised and rented properties since
December 2013 (Decreto-Lei n.º 118/2013).
The matching of the information based on these data flows was done in a stepwise way
using a property cadastral identification number, a unique identification key, which is associated
with each commercial property. Figure 1 illustrates the process.
Figure 1: Summary of the data matching process
Following the classification used by AT for the definitions of commercial properties,
the choice of the data covered the following three strata: Wholesale and retail commerce,
Services, and Industry and warehouses.9 In the matching process, situations in which there
was more than one rental contract or receipt per cadastral identification number were
8
A ninth level, G, was also available prior to the end of 2013. Properties rated with this scale were also found in the data (when an
EPC is issued, it is valid for a period of 10 years).
9
Hereafter referred as Retail, Services and Industry.
7excluded. Records that had no information from the IMI were also ruled out from the
database. The end product of this matching process was a dataset containing more than 2.8
million records from 146,211 rental agreement contracts.
After a preliminary analysis of the data, it was decided exclude atypically high or low
observations using maximum and minimum values for the rent (level and per square meter),
age and area variables. The exclusions are available in the Appendix. As a result of the
application of these restrictions, 4 percent of the observations were excluded from the database
used in the compilation of the property rental index. In total, the final dataset has 2.7 million
receipts, corresponding to 140,943 rental contract agreements.
4.2. Exploratory data analysis
Table 2 provides the descriptive statistics of a group of selected variables. The data
refers to the more than 2.7 million rent receipts, which are available from the 140,943
contracts left in database after the application of the data restrictions described in the
previous section.
Table 2: Descriptive statistics of a group of selected variables
Missing
Variable Mean Median Stdev Obs. (#)
obs. (%)
Rent per square meter (€/sq. m) 5.94 4.69 4.7 2,746,767 0
Retail 6.54 5.15 0.50 1,922,688 0
Services 5.91 4.95 0.40 499,525 0
Industry 2.46 1.98 0.15 324,554 0
Gross floor area (sq. m) 126 78 178.3 2,746,767 0
Retail 85 72 4.9 1,922,688 0
Services 100 67 4.1 499,525 0
Industry 411 300 1.9 324,554 0
Age of property (# of years) 33 26 24.3 2,746,767 0
Length contract information (#) - - - 1,085,313 60.5
Dummy lease 5 years (%) 11.0 0 0.3 118,917 -
Dummy no fixed term (%) 61.9 1 0.5 672,095 -
Energy efficiency rating (#) - - - 436,579 84.1
Dummy A and B rates (%) 28.1 0 60.1 122,663 -
Dummy C rate (%) 41.3 0 99.7 180,188 -
Dummy D, E, F, G rates (%) 30.6 0 374.7 133,728
Receipts per contract (#) 19.5 19 12.1 2,746,767 -
As expected, not all the contracts are present in the 36 months covered by the data. In
fact, the average number of months in which a contract is available in the database is of
19.5 (see the last line of the table). The percentage of rentals present from January 2015 to
the end of 2017 is nevertheless significant (10.6%). Conversely, the number of situations in
8which there is only one observation (receipt) is smaller, accounting for 3.3% of all of
available observations. In general, the descriptive statistics provide a good indication as to
the quality of the data. For instance, industry type of properties simultaneously account for
the lowest average price per square meter (2.46 €) and the highest average area (411 sq. m).
Moreover, the correlations among key price determining characteristics – see the
Appendix -, such as the level of the paid rent, area and property age, display expected signs
(these are +0.508 and -0.045, respectively). The rent level is positively correlated with the
duration of the contract (+0.165), suggesting that there could be a market price premium to
be paid in long-term contracts.
The number of missing values for the length of the contract is very high for the three
years (more than 60 percent). This is a consequence of the data merging process, in which
information on the rentals contract (either on new contracts or on changes and renewals of
already existent ones) has only been made obligatory from the beginning of 2015 onwards.
A more problematic data quality issue has to do with the number of observations with
energy efficiency rates, which is very low (only 15.9 percent).
Figure 2 portraits this data quality issue, where the percentage of observations with
missing information is given on a monthly basis.
Figure 2: Percentage of missing obs. for energy efficiency rating and contract length
As the figure shows, the percentage of missing observations drops from more than 75
percent in January 2015 to slightly more than 50 percent in December 2018. It is expected
that the percentage of missing values for the length of the contract drops even more as more
contracts enter in the database. The high percentage of missing observations for energy
efficiency also drops in a consistent way throughout the years. However, they are still well
9above the 85 percent mark. This situation has to do with the fact that, while the bulk of the
information comes from a single source (AT), information from energy efficiency is taken
from a different source (ADENE), which presents different codification priorities than the
ones of AT (e.g., they tend to pay more attention to the right codification of energy
efficiency ratings than of individual property cadastral identification numbers). However, it
is also expected that the percentage of matched information also increases for this variable
in future as the system of data transmission consolidates.
The information available in the dataset is taken from a total of 116,267 different
properties, located all over the country. Of these, 69.6 percent are labeled as Retail, 18.9
percent as Services, and 11.7 percent as Industry. Although there are no official figures for
commercial properties, it is possible to estimate its stock using the flow of information
received from AT until October 2018. Based on this, the stock is estimated to be of 837,674
units.10 Of these, 46.3 percent were defined as Retail, 28.3 percent as Services and the
remaining 25.4 percent as Industry. This suggests that the sample of rents available for the
compilation of the rent indexes might be overrepresented for Retail. This is a plausible
situation, as the electronic rent receipts do not cover the issuing of rent by companies.
Situations in which individuals issue rent receipts are more likely to exist for the retail
sector (e.g., renting shops) than for Services (e.g., renting offices) or Industry (e.g., a
manufacturing structure). In terms of geographical coverage, the dataset reasonably follows
the structure that is available for the estimated stock of commercial properties.
5. Results
As in any other application involving the compilation of hedonic price indices, the results
can be divided into two groups. In the first one, the quality of regression outputs is analysed
(e.g., the signs, magnitudes, and significance of the estimated parameters of the hedonic
regression). In the second one, the derived indices are presented.
5.1. Regressions
The starting point for the final specification of the hedonic models was the work done for
the RPPI (Raposo and Evangelista, 2017). Following this approach, it was chosen to model each
one of the market segments separately (Retail, Services and Industry). In the modelling process,
the literature review that was carried out in relation to rent determinants was also taken into
10
For means of comparison, and using the same data source and procedure to estimate the stock of residential properties, one
obtains a total of 5,803,961 units. The equivalent figure, taken from the last Census, is 5,859,540.
10account (see section 2). The model specification also benefits from a spatial analysis, performed
via the inclusion of cluster dummies, which group different statistical subsections (parishes in
the case of the industry) in order to capture existent similarities between territorial divisions.
The variables used to group these clusters were the rent and sales values by squared meter for
commercial and housing properties.
The explanatory variable of the models is the logarithm of rent value, obtained from the rent
receipts data. In total, the specification for the Retail includes 54 explanatory variables. For the
Services and Industry models, a total of 47 and 41 covariates were included. The variables used
in these models are shown in the Appendix. For the sake of space, it is only provided the output
(coefficients and results of statistical tests) for a single month (i.e., March 2016). The average
adjusted R-squared obtained from all the regressions was 0.506, ranging from a minimum of
0.373 to a maximum of 0.751. These values are in line with the type of used data (i.e., pooled
cross-sectional) and also in accordance with the values observed in the literature for similar
studies (see Table 1).
5.2. Index results
An issue that was investigated in this work was the degree to which rent index results
stemming from the application of the imputation hedonic method varied when other hedonic
approach was used. Table 3 compares the year on year rates of change obtained for the Retail,
Services and Industry indexes obtained using the time dummy (TD) and imputation (IMP)
hedonic methods.
Table 3: Year on year results of the time dummy and imputation hedonic indices
Retail Services Industry
TD IMP TD IMP TD IMP
2016 -0.36 -0.37 -0.24 -0.22 0.12 0.11
2017 0.13 0.09 0.59 0.59 1.30 1.27
The year on year rates of change are almost identical. In terms of rent development, both
types of indices provide the same picture, with prices of leases of commercial space decreasing
in 2016 (with the exception of Industry) and recovering in 2017. The coherence of the results
was also analysed when the length of contracts, which is one of the variables identified as a key
rent determinant (see section 2.1) and that suffers from quality limitations in our database, was
dropped from the hedonic regression model.
Figure 3 compares the year on year rates of change taken from the rental index in which the
specification of the hedonic price model did not control for contract duration (Whole sample)
11and the one that did include this variable (Subsample). This last index only took into account the
observations that had information on the length of the contract. The results for Retail and
Services are shown in the top panels of the figure. The bottom-left panel of Figure 3 shows the
results for Industry.
Figure 3: Evolution of commercial property rental indexes
As shown in the panels, the biggest discrepancies between the two rates of change are
obtained for 2016, where the percentage of missing observations is above 60% (in 2015, it
reaches 75 percent of observations). In 2017, where the percentage of cases in which there is
information on contract length is below 60 percent, the differences are not so remarkably big.
This suggests that the main reason for the existence of differences is the sample and not the fact
that the hedonic model is not controlling for the length of the contract. This situation is
confirmed when rental indexes were compiled with and without contract length variables using
the subsample of the data in which information for this variable exists. When this happened, the
differences between the rates of change are small.11
The bottom-right panel of Figure 3 displays the year on year rates of change for the CPPI
and CPRI using the whole sample. This last index is the weighted average of the indexes
covering Retail, Services and Industry activities; for the weights, it was used the 2016 total rent
11
This outcome was also observed in an exercise in which contract length was imputed for the whole sample using a hot-deck
procedure.
12value for these three property categories (the applied weights were: 63, 18 and 19 percent,
respectively). The figures show that rents have evolved at lower rates than the sales market (3.3
against 0.4 percent in 2017). However, it should be borne in mind that, while the CPPI is a
sales-based index, the rent index takes into account not only the new rentals, but also the ones
that are already rented.
The possibility of developing sub-indices for particular market segments was also analysed.
Figure 4 presents the results for the group of commercial property rentals in which it was
possible to identify that were located in a shopping centre or in office buildings (there are IMI
codes that allow for the identification of these units). For the sake of comparison, the indexes
are depicted together with the year on year rent change for Retail and Services.
Figure 4: Evolution of commercial property sub-indexes
The sub-indexes are more volatile and with the rates of change systematically lower
than the ones shown by the indexes for Retail and Services. This is somewhat against a
priori expectations. Clearly, more work needs to be done to develop sub-indexes for
particular sectors of the market.
6. Summary and the way forward
This paper provides the results of commercial property rent indexes for Portugal for
2015-2017. The indexes are based on a unique dataset containing a large number of
observations on individual rental contracts. Empirical results are coherent across different
hedonic methods and constitute a good starting point for the future establishment of a
commercial property rent index. The absence of the length of contract variable from the
specification of the hedonic functions underlying the derivation of rent indexes seems not to
produce a major impact on overall outputs.
13The work presents some limitations, which will be revisited in the course of the two
year horizon provided by the grant agreement for the development of real estate statistics. In
particular, it is worth noting three limitations which will ultimately dictate future work in
this area. The first one refers to the fact that, as it was mentioned in section 4.1, the used
data source only partly covers the rental market of commercial space. Although including
the rentals of commercial properties owned by individuals and by sole proprietorship
enterprises, more complex forms of businesses are not taken into account for the calculation
of the indexes. Future work will involve the investigation of possible sources for this
uncovered area of the market (e.g., business statistics data on rents paid by non-individual
enterprises). The second has to do with quality limitations of some variables available in the
existent dataset. However, as it was highlighted above, the quality of received data is likely
to improve in the future as more contracts enter in the database and as data transmission
mechanisms consolidate. Finally, the length of the series (three years) does not allow the
drawing of consistent conclusions as to the ability of the indices to portrait the reality of
rent developments (e.g., its trend, cycles). However, it will be possible to analyze 2018 data
in the next months and until 2020, at least one more year of information will be ready for
research. Despite the current limitations, Statistics Portugal considers that the data already
available is a good basis for the development of a set of commercial property rent indices
and other price indicators such as cap rates.
14Appendix
Exclusions applied to initial dataset
Rent level Gross floor area Rent per sq.m Age
Retail < 50 and > 2,500 € < 10 and > 500 sq.m < 0.5 and > 35 > 250
Services < 50 and > 3,000 € < 15 and > 800 sq.m < 0.4 and > 30 > 200
Industry < 25 and > 5,000 € < 25 and > 2,500 sq.m < 0.15 and > 15 > 250
Correlations between key characteristics of a lease contract (Pearson corr. coef.)
Rent level Gross floor area Property age Lease length
Rent level - 0.508 -0.045 0.165
Gross floor area - -0.075 0.078
Property age - 0.125
Lease length -
15Description of explanatory variables
Variable Variable description
LNGRFA The natural logarithm transformation of gross floor area. The gross floor area corresponds to the
sum of all covered areas, as measured from the outer perimeter of walls, which have the same use
as the property unit.
SQLGRFA Square of LNGRFA.
DLENGTHi Set of 3 dummy variables identifying the rent contract’s term: (1) when the agreement’s length is
greater than 1 year and less or equal than 5 years, (2) for a greater than 5 years length, (3) when
the agreement has no fixed term.
DREGIONi Set of five dummy variables identifying the following geographical areas: (1) North, without the
metropolitan area of Porto (DREGION1), (2) Centro region (DREGION2), (3) Alentejo region
(DREGION3), (4) metropolitan area of Lisboa (DREGION4), (5) metropolitan area of Porto
(DREGION5).
DLX Dummy variable = 1 when the residential unit is located in Lisboa, the capital of Portugal.
DOPORTO Dummy variable = 1 when the residential unit is located in Porto, the second largest city in
Portugal.
DSEA Dummy variable = 1 when a property is located in parish that has access to the sea.
DAIRPORT Dummy variable = 1 when the property is located near an airport (in the same postal code area).
DSEAPORT Dummy variable = 1 when the property is located near a sea port (in the same postal code area).
DNEWPROP Dummy variable = 1 when the reason for delivering the IMI tax declaration is the
acknowledgement (to tax authorities) of a new property (Prédio novo).
DHORZ Dummy variable = 1 when the legal ownership status of the property unit is defined as horizontal
property regime.
DTOTAL Dummy variable = 1 when the ownership status of the property unit is defined as total property
regime (one entity is the owner of the whole property and can rent different factions).
DSINGLE Dummy variable = 1 when the property has a single owner (no co-ownership of the unit).
DAPPRAISALi Set of four dummy variables identifying ranges of property unit values, as they were appraised by
tax authorities: (1) between 50,000 euros and 99,999 euros (DAPPRAISAL1), (2) 100,000 euros
and 149,999 euros (DAPPRAISAL2), (3) between 150,000 euros and 300,000 euros
(DAPPRAISAL3), (4) higher than 300.000 euros (DAPPRAISAL4).
DCONSTRi Set of two dummy variables identifying the building construction technology time period in which
the property unit was first completed: from 1960 to 1989 (DCONSTR1), after 1990
(DCONSTR2).
DQUALOCi Set of three dummy variables identifying the quality of the location, as it is measured by a tax
authorities’ “index” (index = 1 means standard quality; higher than 1 means above than average
quality): from 0.7 to 1 (DQUALOC1), from 1 to 1.3 (DQUALOC2), above 1.3 (DQUALOC3).
DTRANS Dummy variable = 1 if the property was transacted after the year 2009.
DROOMi Set of two dummy variables identifying the number of rooms in a property unit: one room
(DROOM1), five or more rooms (DROOM2).
DDEPAi Set of two dummy variables corresponding to different ranges of dependent areas: (1) greater than
0 and less or equal than 50 m2 (DDEPA1), (2) greater than 50 m2 (DDEPA2). The dependent area
is defined as the sum of all covered areas, including those located outside of the dwelling unit,
which provide accessory services to the main use of that same dwelling unit. Garages, attics and
cellars constitute typical examples of dependent areas.
DPLOT Dummy variable = 1 when the plot area of a property unit is greater than 0. The plot area
corresponds to the total uncovered land area, which is associated with an individual property unit.
DCSYSTEM Dummy variable = 1 when the residential unit includes a central heating and/or air-conditioning
system.
DSHOPMALL Dummy variable = 1 when the property unit is located in a shopping mall.
16Variable Variable description
DOFFICE Dummy variable = 1 when the property is located in an office building.
DCONSTQ Dummy variable = 1 when the construction quality of the property unit (e.g., quality of the
project, thermal insulation, acoustic insulation, quality of building materials used at latter
construction works phases) is good.
DVIEW Dummy variable =1 when the visual prominence of the location in which the property unit is
located is high. This element should not be confused with DQUALOC, as the former essentially
measures the scenic value of the location (e.g., in front of the sea) and the latter the quality of
public and private services and goods available in the area.
DGOODINTLOC Dummy variable = 1 when the property has a good location inside the building in which it is
located.
DBADINTLOC Dummy variable = 1 when the property has a bad location inside the building in which it is
located.
DZEROPOS Dummy variable = l when the property unit has been signalled with no positive attribute by the tax
authorities’ appraisal exercise. Examples include: existence of a lift or escalator, good
construction quality and access to air conditioning facilities.
DZERONEG Dummy variable = 1 when the property unit has been signalled with no negative attribute by the
tax authorities’ appraisal exercise. Examples include the following characteristics: no access to
water or electric power, absence of paved streets, and bad conservation state of the building.
DRETSUBSi A set of five dummy variables for clusters of the retail market segment, built based on the
univariate local Moran's I measure (for overall spatial autocorrelation). The clusters (for the retail
segment) were constructed at a subsection level and based on the value rent/m2.
DRETSECi A set of two dummy variables clustering (Census 2011) portions of the territory. The clusters were
constructed at a section level and based on five variables: number of retail rent receipts, average
housing transaction value, average retail properties transaction value, average retail rent value,
share of retail rent receipts.
SQRMORTG Square root transformation of average monthly mortgage charge resulting from the purchase of
dwellings. Census 2011 data, at section level.
SQRELECTR Square root transformation of the percentage of dwellings with electricity as main source of
energy used for heating. Census 2011 data, at section level.
SQRHEIGHT Square root transformation of the share of buildings with 5 or more floors. Census 2011 data, at
section level.
SQRCOLLECT Square root transformation of the share of collective buildings. Census 2011, at section level.
SQREMPLOY Square root transformation of the share of employers, as occupational status. Census 2011, at
section level.
SQRNATION Square root transformation of an index for nationality composition: the higher its value, the higher
the difference with the nationality structure of the resident population in Portugal. Census 2011
data, at section level.
DSERSUBi A set of five dummy variables for clusters of the service market segment, built based on the
univariate local Moran's I measure (for overall spatial autocorrelation). The clusters (for the
services segment) were constructed at a subsection level and based on the value of rent/m2.
DSERSECi A set of three dummy variables clustering portions of the territory. The clusters were constructed
at a section level and based in the value rent/m2 for properties with a retail use.
DINDi A set of two dummy variables clustering portions of the territory. The clusters were constructed at
a parish level and based in the value of transaction/m2. Only properties with a retail use were
considered.
17Coefficient estimates for the Retail, Services and Industry models (March 2016) Explanatory Variables Retail Services Industry Intercept 1.955*** 2.112*** 1.323*** LNGRFA 0.805*** 1.087*** 0.99*** SQLGRFA -0.042*** -0.063*** -0.046*** DLENGTH1 0.051*** 0.062* 0.13** DLENGTH2 0.166*** 0.128*** 0.251*** DLENGTH3 0.091*** 0.082** 0.226*** DREGION1 -0.078*** -0.247*** -0.081 DREGION2 -0.009 -0.285*** -0.131** DREGION3 0.019 -0.178*** -0.082 DREGION4 -0.08*** -0.035 -0.04 DREGION5 0.051*** -0.222*** 0.011 DLX -0.005 0.222*** 0.094 DOPORTO -0.009 0.06* -0.15 DSEA 0.019** 0.041* 0.03 DAIRPORT 0.145*** 0.108 0.119 DSEAPORT 0.007 -0.116*** 0.106 DNEWPROP 0.074*** 0.049 0.008 DHORZ 0.06*** 0.049** 0.051* DTOTAL - - -0.065* DSINGLE 0.028** 0.053** 0.006 DAPPRAISAL1 0.073*** 0.109*** 0.033 DAPPRAISAL2 0.17*** 0.221*** 0.11* DAPPRAISAL3 0.289*** 0.361*** 0.316*** DAPPRAISAL4 0.323*** 0.421*** 0.437*** DCONSTR1 -0.02* -0.038 -0.029 DCONSTR2 0.031** 0.001 0.112*** DQUALOC1 0.065*** 0.099*** 0.073** DQUALOC2 0.116*** 0.105*** 0.184*** DQUALOC3 0.152*** 0.128*** 0.283*** DTRANS 0.026** 0.099*** 0.048 DROOM1 -0.008 0.031 -0.042 DROOM2 -0.067* -0.028 -0.048 DDEPA1 0.074*** 0.051*** 0.108** DDEPA2 0.17*** 0.106*** 0.143*** DPLOT 0.034** -0.004 0.04 DCSYSTEM 0.143*** 0.075 - DSHOPMALL -0.054** - - DOFFICE - -0,11*** - DCONSTQ 0.005 -0.039 - DVIEW -0.002 -0.093 0.36 DGOODINTLOC 0.065*** - - DBADINTLOC -0.262*** - - DZEROPOS - -0.146*** 0.134 DZERONEG 0.037*** 0.091*** 0.093*** DRETSUBS1 0.042*** - - DRETSUBS2 -0.125*** - - DRETSUBS3 -0.162*** - - DRETSUBS4 0.238*** - - DRETSUBS5 -0.082 - - DRETSEC1 0.332*** - - DRETSEC2 0.109*** - - SQRMORTG 0.015*** - - SQRELECTR 0.035*** - - SQRHEIGHT 0.006*** - - SQRCOLLECT 0.042*** - - SQREMPLOY 0.043*** - - SQRNATION 0.021*** - - DSERSUB1 - -0.227*** - DSERSUB2 - -0.253*** - DSERSUB3 - 0.14*** - DSERSUB4 - -0.066 - DSERSEC1 - 0.072*** - DSERSEC2 - 0.22*** - DSERSEC3 - 0.311*** - DIND1 - - 0.219*** DIND2 - - 0.228*** n 20,526 4,393 2,953 Adjusted R2 0.4354 0.5234 0.4830 *** p
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