Accessibility by public transport predicts residential real estate prices:
a case study in Helsinki region
Indrė Žliobaitė INDRE . ZLIOBAITE @ AALTO . FI
Michael Mathioudakis MICHAEL . MATHIOUDAKIS @ HIIT. FI
Tuukka Lehtiniemi TUUKKA . LEHTINIEMI @ HIIT. FI
Pekka Parviainen PEKKA . PARVIAINEN @ AALTO . FI
Tomi Janhunen TOMI . JANHUNEN @ AALTO . FI
Helsinki Institute for Information Technology (HIIT), Espoo, FINLAND
Aalto University, Dept. of Computer Science, Espoo, FINLAND
Abstract lates to the distance to the city center. Professional real es-
tate price models include many more features of apartments
This pilot study investigates how considering ac-
and environment, such as age, construction type, floor, or
cessibility could help to model prices of residen-
population characteristics in the neighborhood.
tial real estate more accurately. We introduce
two novelties from the price modeling point of Residential real estate prices are typically modeled using
view (1) defining accessibility as travel time by so called hedonic models (Case & Quigley, 1991; Sirmans
public transport, in addition to geographic dis- et al., 2005), where the price of a house is assumed to be
tance, and (2) considering dynamic points of in- affected by the structural characteristics of the house it-
terest from check-ins into social networks, in ad- self, characteristics of the neighborhood, and environmen-
dition to fixed location community centers. Our tal characteristics. While in real estate domain research
case study focuses on the Helsinki region. We mainly focuses on identifying factors that impact pricing,
model price per square meter as a linear function in machine learning and data mining research real estate
of apartment characteristics, and characteristics price modeling mainly focuses on developing sophisticated
of the neighborhood, including accessibility by predictive models beyond linear regression (Chopra et al.,
public transport and social activities. The result- 2007; Fu et al., 2014).
ing models show good predictive performance,
A literature review on hedonic pricing models
as compared to baselines not taking accessibility
(Bartholomew & Ewing, 2011) finds the structural
into account. We discover that apartment price
characteristics typically include the age and the size of
relates to the geographical distance from the city
the house, the number of bedrooms, and the presence of
center, but accessibility by public transport to lo-
different amenities such as a garage. The effect of the
cal centers of interest is more informative than
location of the house on housing prices is often captured by
just the geographical distance to those centers.
physical proximity to a central business district (CBD) or a
regional center. The literature review finds evidence of an
inverse relationship between pricing and distance to CBD
1. Introduction in studies on various cities around the world. Another
Modeling real estate prices has long been of interest to re- access-related characteristic often used in hedonic models
searchers and practitioners, and it is employed for various is the proximity of the house to a transit station, measured
purposes related to investment, lending or taxation. Ar- in air distance or walking distance. This attribute is used to
guably all city residents, even non-specialists, intuitively capture the effect transit has on relative accessibility of a
understand that the price of a residential apartment posi- CBD or a regional center. Here the results are more mixed,
tively relates to the size of the apartment, and negatively re- with the majority of studies suggesting pricing premiums
for housing located near to a transit station, and a higher
Proceedings of the 2 nd International Workshop on Mining Urban premium for transit stations that provide a higher degree of
Data, Lille, France, 2015. Copyright c 2015 for this paper by its relative proximity to a CBD.
authors. Copying permitted for private and academic purposes.
e8
Accessibility predicts real estate prices in Helsinki
The era of big data provides access to new data sources,
such as public transport, traffic and social mobility data,
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factors when buying an apartment). Integrating such data
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could help to model residential real estate prices more pre- ●●
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overall development of modern smart cities.
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Our pilot study investigates to what extent accessibility of a ●● ●● ● ● ● ●●●
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neighborhood relates to residential real estate prices. This ●●
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case study focuses on the Helsinki region. We model price
●●
per square meter as a linear function of apartment charac-
teristics, and characteristics of the neighborhood, including
accessibility by public transport and social activities. Our
main hypothesis is that prices are more related to travel
times than travel distances, and local centers of activities
than the city center. The resulting models show good pre- Figure 1. Location of apartments in the dataset. The black rect-
dictive performance, as compared to baselines not taking angle indicates the area from which point of interest data is col-
accessibility into account. We discover that an apartment lected.
price relates to the geographical distance from the city cen-
ter, but accessibility by public transport to local centers of
interest is more informative than just the geographical dis- 2.1. Real estate data
tance to those centers.
The sales price data comes from a Finnish web portal
Our study introduces two conceptual novelties in modeling Oikotie3 , which is the most popular marketplace for resi-
prices of residential real estate: (1) to measure accessibil- dential real estates sales and rental. Our dataset consists
ity, we consider travel times in addition to distances, and of apartments in the capital region (Helsinki, Espoo, Van-
(2) we consider dynamic local points of interest, defined by taa and Kauniainen municipalities) advertised for sales on
4square1 check-ins (people posting their location and activ- October 24, 2014. The pricing data is based on sales ads,
ity on a social network), in addition to community centers as sales transaction prices are not available for the public.
at fixed locations. We exclude apartments that do not provide a street address
The remainder of the paper is organized as follows. Section (hence no coordinates), and for which size is not available.
2 describes data acquisition and feature engineering. Sec- Moreover, we filter out very large apartments (size more
tion 3 presents the results of the experimental case study, than 300 m2), very old apartments (built earlier than 1850),
and Section 4 concludes the study. far away apartments (distance to metro more than 20 km),
extremely cheap (price pr square meter less than 1200 eur)
and extremely expensive apartments (price per square me-
2. Data acquisition and feature engineering ter more than 12000 eur), because we aim at focusing on
Our dataset consists of three parts: real estate data describ- modeling prices of mainstream apartments and avoiding
ing characteristics of the apartments, location data describ- extreme outliers. After filtering our dataset includes 8337
ing points of interest and community centers, and acces- apartments. Figure 1 plots all the apartment locations.
sibility data describing point-to-point distances and travel
times. We make our dataset publicly available2 for re- 2.2. Location data
search. We consider two types of location data: fixed location,
1
http://foursquare.com and dynamic points of interest. Fixed location data in-
2
http://www.zliobaite.com/datahel.zip cludes the city center, for which the Stockmann depart-
ment store is used as a proxy (coordinates found by hand
via Google maps), and local community centers, approx-
imated by H&M shop (a chain of clothing shops) loca-
tions in Helsinki region (also found by hand from Google
3
http://www.oikotie.fi/
ee
Accessibility predicts real estate prices in Helsinki
HM
●
HM
●
HM
● HM
●
HM
HM
HM ●
●
●
● Stockm
Stockman
ckm an
Figure 2. Fixed locations: community centers (H&M) and city Figure 3. Dynamic points of interest from 4square: blue 2:00-
center (Stockmann). The black rectangular indicates the area from 6:00, violet 6:00-10:00, red 10:00-14:00, brown 14:00-18:00, or-
which point of interest data is collected. ange 18:00-22:00.
maps). Stockmann is a well-known location in the centre of the apartment to coordinate of the point of interest, as
Helsinki. H&M shops are typically present in larger shop-
ping malls. Shopping malls are local centers of attraction.
We hope that H&M serves as a proxy for local centers in D = Re · arccos(s1 + s2 ), where
the neighborhoods. Figure 2 plots the community centers s1 = cos(lat 1 ) ∗ cos(lat 2 ) ∗ cos(lon 2 − lon 1 ),
and the city center location.
s2 = sin(lat 1 ) ∗ sin(lat 2 ),
Dynamic points of interest are obtained from an existing
dataset of 4square check-ins (Le Falher et al., 2015). Each
check-in in the dataset corresponds to one user’s visit to where Re is the radius of Earth (set to Re = 6371km),
one venue (restaurant, cafeteria, store, etc) with known ge- (lat 1 , lon 1 ) are the coordinates of the apartment, and
ographic location, at a particular time. The data cover user (lat 2 , lon 2 ) are the coordinates of the point of interest.
activity between March and July 2014 in the inner Helsinki
Travel time by public transport between two coordinates is
city. To extract points of interest, we perform k-means
measured using a freely available tool Reititin4 , developed
clustering on the geographic locations of check-ins, using
by BusFaster Ltd and researchers at University of Helsinki.
k = 20. Each of the k centroids identified defines one point
We use the default settings.
of interest. Note that we extract points of interest both on
top of all check-ins contained in the dataset, regardless of In addition to accessibility between apartments and points
the the time of the day they occur, as well as separately for of interest we also include the distance from an apartment
check-ins that occur at separate time intervals in the day to the nearest metro station. The address of Metro stations
(five 4-hour intervals from 2am to 10pm ). Figure 3 plots is listed on Helsinki Metro’s website5 and their geographic
the points of interest for each time interval. coordinates are collected via manual queries to the Google
Maps API6 . Note that in the Helsinki region metro runs
2.3. Accessibility features only to the eastern part of the city, therefore, we do not
necessarily expect a regular behavior from this feature. A
Accessibility data connects apartments with point of inter- regular behavior would be a higher price if there is a metro
est. We consider two types of accessibility features: air stop nearby.
distance from an apartment to the location of a point of
4
interest, and travel time by public transport from an apart- http://blogs.helsinki.fi/saavutettavuus/
ment to the point of interest (including walking time). tyokaluja/metropaccess-reititin/
5
http://www.hel.fi/hki/hkl/en/HKL+Metro
6
Air distance is measured in kilometers from coordinate of https://developers.google.com/maps/
ed
Accessibility predicts real estate prices in Helsinki
Table 1. Input features (predictors).
Feature Description Units ● ●
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6000 10000
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6000 10000
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size apartment size m2
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price per m2
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price per m2
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year
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year built -
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fyear
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north
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northerness km
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2000
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2000
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ametro metro within 1 km (0,1) - 50 150 250 0 5 10 15 20
dstock Stockmann distance km size metro distance
tstock Stockmann travel time min
dhm distance to H&M km
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6000 10000
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6000 10000
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price per m2
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d4sq3
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distance to center 10-14:00 km
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d4sq4 distance to center 14-18:00 km 1900 1950 2000 0 4000 8000 12000
d4sq5 distance to center 18-22:00 km construction year f!year
t4sq1 travel time to center 2-6:00 min
t4sq2 travel time to center 6-10:00 min
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travel time to center 10-14:00 min
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t4sq4
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travel time to center 14-18:00 min
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travel time to center 18-22:00 min
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2.4. Final set of predictors !20 !10 0 10 0 5 10 15 20 25 30
Easternness No he nness
Altogether we consider 23 input features (predictors) for
6000 10000
6000 10000
modeling apartment prices. The features are listed in Ta-
p ce pe m2
p ce pe m2
ble 1.
Feature fyear is a non-linear transformation of the con-
struction year, which is based on observation that apart-
2000
2000
ments built around 1970 are the least valuable, because a
major pipe renovation is due in about 50 years from initial 0 5 10 15 20 0 20 40 60 80 100
S ockman d s ance S ockman ave me
construction. Pipe renovation is done for the whole house
at once, and brings substantial expenses for the apartment
6000 10000
6000 10000
owners. We define the derived feature as
p ce pe m2
p ce pe m2
fyear = (year − 1970)2 , which puts very new and very
old apartments together, while apartments that are around
50 years old are put on the opposite end.
2000
2000
Easternness and northerness features try to capture another
peculiarity of the Helsinki region, where apartments in the 0 2 4 6 8 10 12 0 20 40 60 80
HM d s ance HM ave me
east are on average considered cheaper. South apartments
may be considered more expensive due to proximity to the
6000 10000
6000 10000
sea.
p ce pe m2
p ce pe m2
Figure 4 plots the values of selected input features against
the target variable price per square meter for visual inspec-
2000
2000
tion. General tendencies are consistent with common intu-
ition. The smaller the apartment, the higher the price per
m2. Apartments close to metro are more expensive. The 0 5 10 15 0 20 40 60 80
4squa e d s ance 4squa e ave me
cheapest apartments have been constructed around year
1970. The most expensive apartments are in the city cen- F gu e 4 npu ea u es aga ns he a ge va ab e
ter, and apartments near to the local community centers or
points of interest are more expensive.
e3
price_per_m2
Accessibility predicts real estate prices in Helsinki
interpretable. Note, however, that some of the features are
dmetro
ametro
dstock
d4sq1
d4sq2
d4sq3
d4sq4
d4sq5
tstock
t4sq1
t4sq2
t4sq3
t4sq4
t4sq5
north expressed as non-linear functions of simpler features (e.g.
fyear
d4sq
dhm
year
east
t4sq
size
thm
1
price_per_m2 100 !15 !1 57 !6 !52 !40 23 !62 !52 !37 !29 !43 !50 !45 !44 !45 !43 !43 !53 !52 !50 !48 !52
size !15 100 2 !4 !6 !2 11 !9 11 14 7 11 13 19 14 14 14 13 13 19 19 20 19 20
fyear is a non-linear function of a building’s age, as ex-
year !1 2 100 16 !16 15 32 !29 38 41 18 17 30 35 31 31 30 30 30 35 33 32 36 36 0.8 plained above). The ordinary least squares procedure (the
fyear 57 !4 16 100 !6 !17 !10 14 !22 !16 !15 !13 !12 !13 !13 !13 !13 !12 !13 !17 !16 !15 !13 !16
east !6 !6 !16 !6 100 42 !67 34 !17 !23 6 14 !29 !23 !32 !33 !29 !29 !28 !15 !10 !23 !28 !16
standard implementation in R) is used for estimating the
0.6
north !52 !2 15 !17 42 100 31 !21 61 55 49 54 32 47 31 30 35 32 31 50 50 46 46 46 model parameters.
dmetro !40 11 32 !10 !67 31 100 !53 80 74 45 37 77 76 79 79 79 77 76 70 65 74 78 70
0.4
ametro 23 !9 !29 14 34 !21 !53 100 !38 !41 !30 !28 !26 !33 !26 !27 !27 !26 !25 !30 !25 !31 !36 !30
dstock !62 11 38 !22 !17 61 80 !38 100 80 62 51 91 87 91 91 92 90 90 85 84 84 87 86
For assessing the performance we use two common accu-
tstock !52 14 41 !16 !23 55 74 !41 80 100 54 59 66 87 70 69 69 66 65 87 86 87 87 86 0.2 racy measures: coefficient of determination (R2) and mean
dhm !37 7 18 !15 6 49 45 !30 62 54 100 84 59 52 58 58 60 58 57 53 53 49 53 51
thm !29 11 17 !13 14 54 37 !28 51 59 84 100 44 54 44 44 47 45 43 56 55 53 55 52
absolute error (MAE). Coefficient of determination is a rel-
0
d4sq !43 13 30 !12 !29 32 77 !26 91 66 59 44 100 83 99 99 99 100 100 80 79 80 84 83 ative accuracy measure, where 1 means the best possible
t4sq !50 19 35 !13 !23 47 76 !33 87 87 52 54 83 100 85 85 85 83 82 97 96 98 98 97
d4sq1 !45 14 31 !13 !32 31 79 !26 91 70 58 44 99 85 100 100 100 99 99 82 81 82 85 85 !0.2 performance, and 0 means the performance is equivalent to
d4sq2 !44 14 31 !13 !33 30 79 !27 91 69 58 44 99 85 100 100 99 99 99 81 82 83 85 85
random. Mean absolute error indicates error in the units of
d4sq3 !45 14 30 !13 !29 35 79 !27 92 69 60 47 99 85 100 99 100 99 99 82 82 83 86 85
d4sq4 !43 13 30 !12 !29 32 77 !26 90 66 58 45 100 83 99 99 99 100 100 79 79 79 84 83
!0.4
the target variable, 0 is an ideal performance, the higher the
d4sq5 !43 13 30 !13 !28 31 76 !25 90 65 57 43 100 82 99 99 99 100 100 79 79 79 83 83
t4sq1 !53 19 35 !17 !15 50 70 !30 85 87 53 56 80 97 82 81 82 79 79 100 95 96 95 96
!0.6
MAE, the worse the performance.
t4sq2 !52 19 33 !16 !10 50 65 !25 84 86 53 55 79 96 81 82 82 79 79 95 100 96 93 96
t4sq3 !50 20 32 !15 !23 46 74 !31 84 87 49 53 80 98 82 83 83 79 79 96 96 100 95 96 !0.8
We report R2 and MAE measured on the whole dataset
t4sq4 !48 19 36 !13 !28 46 78 !36 87 87 53 55 84 98 85 85 86 84 83 95 93 95 100 96
t4sq5 !52 20 36 !16 !16 46 70 !30 86 86 51 52 83 97 85 85 85 83 83 96 96 96 96 100
used for model fitting (fit) and via 10 fold cross-validation
!1
(cv), which iteratively fits a model on 90% of the data, and
tests on the remaining part. Cross-validation scores provide
Figure 5. Correlations of features (a number indicates the Pearson
correlation coefficient ×100.
an indication of how models would generalize to unseen
data.
3.2. Performance of base models
Finally, Figure 5 plots correlations across all the features.
We see that the location features are strongly correlated Base models do not use any accessibility information, and
with each other, therefore, many may be redundant. Never- use only very basic location information. The first base
theless, some of those could potentially be expected to be model (Size-year) does not use location at all, and is based
more informative than others, therefore, we consider them only on size of the apartment and its construction year
all. We can also see than most of the location features are (fyear ). The second model in addition uses basic loca-
negatively correlated with community centers and dynamic tion information, encoded as raw geographical coordinates,
points of interest. We already have seen similar tendencies centered in the old town of the city.
in the scatterplots. This behavior is along with a common
The resulting models for price per square meter are:
intuition that apartments near points of interest should be
more expensive. price = 3722 − 4.97 × size + 0.91 × fyear ,
The correlation and scatter plots analyzed features one-by-
one. In the next section we will consider predictive models and
that use sets of features for modeling apartment prices.
price = 5643 − 5.14 × size + 0.78 × fyear +
+ 38.9 × east − 147.7 × north.
3. Case study
The goal of this pilot case study is to investigate whether The models are consistent with common intuition: the
accessibility information helps to model real estate prices, larger the apartment, the cheaper the price per square me-
as compared to using only geographical location informa- ter; older or newer apartments with respect to 1970 con-
tion. In addition, we investigate informativeness of dy- struction year are more expensive; the further to the north
namic points of interest (derived from social networks) as from the sea and the city center, the cheaper. Easterness
opposed to stationary fixed points of interest. has a positive effect, which is somewhat inconsistent with
a common intuition that cheaper neighborhoods are in the
east. However, this can be explained by the range of data
3.1. Experimental protocol
(see Figure 4). Data extends further to the west than to the
We model price per square meter. An alternative would east, therefore, western apartments are on average further
be to model the total price. We choose the former as the from the center, and thus cheaper.
target variable, because price per square meter is easier to
Table 2 reports predictive accuracies of the base models.
interpret and compare across neighborhoods.
We can make two observations. First, Size-year-location
We limit our analysis to linear regression, which is easily model already performs quite well with the cross-validation
eN
Accessibility predicts real estate prices in Helsinki
closer to metro, the more expensive is the apartment:
Table 2. Base models for predicting price per m2. R2 - coefficient
of determination (the higher, the better), MAE - mean absolute price = 5300 − 3.94 × size + 0.77 × fyear −
error (the smaller the better).
Model R2 fit MAE fit R2 test MAE test − 62.2 × east − 50.8 × north − 158.4 × metro.
Size-year 0.34 1062 0.33 1063
Size-year-location 0.56 836 0.55 837 Adding metro distance shrinks other coefficients, which
suggests that earlier this feature was indirectly captured.
More importantly, adding metro distance changes the di-
rection of the easternness coefficient from positive to neg-
Table 3. Accessibility for predicting price per m2. Size-year-
ative. Now it is more intuitive keeping in mind peculiar-
location model with one additional accessibility feature at a time.
Add feature R2 fit MAE fit R2 test MAE test ities of Helsinki residential neighborhoods, where overall
Metro distance 0.58 811 0.58 813 the east is considered cheaper than the west.
Metro access 0.56 835 0.55 836
H&M distance 0.56 837 0.56 838
Stockmann distance is as well intuitive - the closer to the
H&M travel time 0.56 831 0.56 832 center, the more expensive is the apartment:
Stockmann distance 0.61 781 0.61 782
Stockmann t. t. 0.58 811 0.58 812 price = 5698 − 3.78 × size + 0.72 × fyear +
4square dist. all 0.58 813 0.58 814 − 3.3 × east − 59.8 × north − 117.8 × dstock .
4square t. t. all 0.58 804 0.58 805
4square dist. peak 0.59 807 0.59 809
4square t. t. peak 0.59 799 0.59 800 Shortest H&M travel time is intuitive - the shorter the travel
time to the local center, the more expensive is the apart-
ment:
R2 result 0.55. Second, the fit and the cross-validation per- price = 5681 − 5.00 × size + 0.78 × fyear +
formance differs only a little, which suggests that there is + 37.0 × east − 139.6 × north − 39.3 × thm.
no notable overfitting, and the model could use more infor-
mative input features.
Shortest 4square travel time is intuitive - the shorter the
travel time to a center of interest, the more expensive is the
3.3. Predictive power of accessibility
apartment:
Next we test whether adding accessibility information
helps to predict more accurately. We test accessibility to price = 5659 − 3.55 × size + 0.77 × fyear +
the city center (Stockmann), local centers (HM), and dy- + 13.5 × east − 103.3 × north − 31.4 × t4sq.
namic centers of interest (4square check-ins) overall and at
morning peak times (from 6:00 to 10:00). We compare in- 3.4. Final predictive model - everything together
formativeness of using air distance as a feature to using the
total travel time by public transport. Finally, we collect a set of promising features into one final
model, and test its performance. The final model includes
We use the base model Size-year-location as a starting the base model (Size-year-location), metro distance, Stock-
point, add one feature at a time to it, and measure the accu- mann distance (a proxy for distance to the city center), and
racy. Table 3 reports the results. travel times to H&M and 4square (peak) local centers.
From the resulting accuracies we can see that accessibil- While the final feature selection is done after seeing the in-
ity has some predictive power, as in all cases the predictive termediate performance results, the fit accuracies have been
performance improves as compared to the base model. The very similar to those of cross-validation, therefore the risk
results indicate that the distance to the city center (Stock- of overfitting is not high.
mann) is more informative than the travel time by pub-
lic transport. However, accessibility to the local centers The final model fitted on all the data is
(fixed centers H&M and dynamic centers 4square) by pub-
price = 5729 − 4.06 × size + 0.71 × fyear +
lic transport is more informative than just the air distance
to those centers. In other words, it seems that an apartment + 31.6 × east − 94.5 × north +
price relates to the overall geographical location, but acces- + 73.0 × metro − 139.0 × dstock +
sibility to local centers of interest is more important than + 21.6 × thm − 12.5 × t4sq2
just the geographical distance to those centers. This is an
interesting finding for exploring in detail in future studies.
We can see some interesting relations, reflecting peculiar-
We report selected models. Metro distance is intuitive - the ities of the Helsinki region. First, the longer the metro
dy
Accessibility predicts real estate prices in Helsinki
ported by the Aalto University AEF research programme
Table 4. Final model for predicting price per m2.
Model R2 fit MAE fit R2 test MAE test and the Academy of Finland grants 118653 (ALGODAN)
Final model 0.63 752 0.62 753 and 251170 (Finnish Centre of Excellence in Computa-
tional Inference Research COIN). Maps are credited to
c OpenStreetMap contributors, for more information see
distance, the higher the price, while one could expect the http://www.openstreetmap.org/copyright.
opposite. Our interpretation is that the metro distance cap-
tures what was not very successfully captured by the east-
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dR