This document discusses research exploring the determinants of liquidity (time-on-market) for rental properties in major German real estate markets using big data and survival analysis techniques. The research was motivated by both practical concerns for private landlords and gaps in the academic literature. Data on over 335,000 rental listings across 7 cities was analyzed using a Cox proportional hazards model to identify characteristics influencing time-on-market. Results showed heterogeneity across markets and identified attributes like rent, area, amenities and location variables as significant drivers of liquidity. The study provides additional insights into real estate liquidity beyond traditional measures.

1. Exploring the determinants of liquidity with big data – Market
heterogeneity in German markets
Marcelo Cajias, Patrizia Immobilien AG
Philipp Freudenreich, International Real Estate Business School IRE|BS, University of Regensburg
Delft, June 29th, 2017

2. 2
Agenda
1. Research Motivation Part 1 – Practical Approach
2. Research Motivation Part 2 – Academic Approach
3. Data and Methodology – Big Data and Cox Proportional Hazards Model
4. Results and Implications

3. 3
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenMotivation Emerging From Real World CircumstancesMotivation Emerging From Real World Circumstances
Research Motivation Part 1 – The Practical Approach
• Extraordinarily low homeownership rate of approx. 45% in Germany
subsidized housing after WWII, low tax benefits, very regulated rental market (Voigtlaender 2009)
• More than half (approx. 60%) of all leasehold housing owned by individuals in Germany
construction loans for subsidized housing available to everyone, retirement provision (Voigtlaender 2009)
• Polycentric structure – seven major real estate markets in Germany
division into four zones after WWII, federal system
How long will these mainly private landlords have to market their dwellings and which attributes
shorten/ lengthen the process?

4. 4
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenMotivation Emerging From Academic LiteratureMotivation Emerging From Academic Literature
Research Motivation Part 2 – The Academic Approach
• Kluger and Miller (1990) introduced the Cox Proportional Hazards (CPH) model to measure the liquidity
of home sales
• Further milestone articles are e.g. Haurin (1988), Krainer (1999) and Anglin et al. (2003)
• In more recent years, the influence of spatial effects (neighbourhoods, school districts, etc.) on pricing of
residential real estate was observed
• Measuring location with coordinates has not yet been used for time-on-market studies
• Until Allen et al. (2009), only the time to sale of property was examined
• Cajias et al. (2015) first to study on time-on-market for rental dwellings in Germany in a “green“ context
Research gap: What additional information for estimating the time-on-market is provided by
including spatial gravity variables in a Cox Proportional Hazards Model?

5. 5
Top Seven Real Estate MarketsTop Seven Real Estate Markets DatasetDataset
Data and Methodology – Big Data and the Cox
Proportional Hazards Model (1)
• 335,972 observations for top-seven cities
• 11 characteristic variables (rent, area, age, parking
slot, elevator, etc.)
• Degree of atypicality from Haurin (1988) and degree
of overpricing from Anglin et al. (2003)
• Spatial variables (longitude, latitude, distance to ZIP
centroid, distance to NUTS3 centroid)
• Various control variables (socioeconomic variables,
age-, ZIP- and time-fixed effects)

6. 6
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenSurvival Analysis – The Cox Proportional Hazards (CPH) ModelSurvival Analysis – The Cox Proportional Hazards (CPH) Model
Data and Methodology – Big Data and the Cox
Proportional Hazards Model (2)
• Was mainly used in epidemiologic and social sciences, as well as in medicine
• Yields the hazard rate (rate of mortality) of an individual here, “death“ is the end of marketing period
• Quantitative and qualitative variables can be applied to explain this rate
• Odds ratio to compare individuals with different characteristics
• Peculiarities of the CPH:
− No need to specify the distribution of the baseline hazard
− Controls for right censoring
− Estimates an event probability per unit of time

7. 7
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenThe Final EquationThe Final Equation
Data and Methodology – Big Data and the Cox
Proportional Hazards Model (3)
= Characteristics of dwelling i in period p
= Socioeconomic factors in region j
= Time fixed-effects
= Spatial fixed-effects
= Smoothing function for rent dwelling i in period p
= Smoothing function for age dwelling i in period p

8. 8
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenSurvival Functions Across the Top-7 Real Estate MarketsSurvival Functions Across the Top-7 Real Estate Markets
Results and Implications (1)
Survival Probability by Quarters Change in Survival Probability by Quarters
The cities behave differently: What drives the time-on-market for the respective cities?

9. 9
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenWhat Drives the Time-on-Market?What Drives the Time-on-Market?
Results and Implications (2)
Berlin Frankfurt Munich Stuttgart Cologne Dusseldorf Hamburg
Rent
Living area
No. of rooms
First occup.
Bathtub
Built-in kitchen
Parking slot
Terrace
Balcony
Elevator
Atypicality
Overpricing
Dist. centr. ZIP
Dist. centr. NUTS
= Longer survival, longer TOM = Shorter survival, shorter TOM = Direction of insignificant effect

10. 10
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenContrary Observations in Berlin and MunichContrary Observations in Berlin and Munich
Results and Implications (3)
Berlin Munich
Munich liquidity

11. 11
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenGeological Constraints in Hamburg and StuttgartGeological Constraints in Hamburg and Stuttgart
Results and Implications (4)
Hamburg Stuttgart

12. 12
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenConcluding RemarksConcluding Remarks
Results and Implications (5)
• Investigation combining an exclusive dataset with an innovative approach
• Identification of major liquidity drivers of rental flats
• Improvement in model specification (pseudo R² Kendall‘s Tau) by introducing coordinates, degree of
overpricing and degree of atypicality by 1.58% to 4.43%
• Additional inclusion of spatial gravity variables and all control variables results in 3.95% to 7.22%
higher concordance measure compared to base model
• Identification of different demand patterns across the cities

13. Thank you for your attention!

14. 14
Textmasterformate durch Klicken bearbeitenTextmasterformate durch Klicken bearbeitenCPH OutputCPH Output
Back-up
Exhibit 3: Semiparametric proportional cox-hazard regression – Y=Time-on-market in weeks
Exp(coefficient)
Mean / (P-Values)
Berlin Frankfurt Munich Stuttgart Cologne Dusseldorf Hamburg
Model I II III I II III I II III I II III I II III I II III I II III
Log rent
-0,056
0,002***
0,016
0,003***
-0,045
0,007***
-0,053
0,001***
-0,014
0,003***
-0,035
0,006***
-0,048
0,002***
0,002
0,005
-0,11
0,009***
-0,054
0,005***
0,088
0,012***
-0,093
0,017***
-0,019
0,003***
0,09
0,007***
-0,085
0,014***
-0,052
0,003***
0,051
0,007***
-0,035
0,011***
-0,045
0,002***
0,036
0,005***
0,003
0,008
Log area
-0,008
0***
-0,01
0***
-0,014
0***
-0,011
0***
-0,012
0***
-0,015
0,001***
-0,011
0***
-0,011
0***
-0,017
0,001***
-0,01
0,001***
-0,01
0,001***
-0,016
0,001***
-0,009
0***
-0,009
0***
-0,015
0,001***
-0,008
0***
-0,01
0***
-0,011
0***
-0,009
0***
-0,011
0***
-0,014
0***
Age
0,002
0***
0
0***
0,012
0,001***
0,001
0***
0,001
0***
0,014
0,003***
0,001
0***
0,001
0***
0,003
0,002
0,001
0
0,001
0
-0,005
0,004
0,001
0***
0
0
-0,005
0,002**
0
0
0
0
0
0,002
0,001
0***
0
0
0,002
0,002
Number of rooms
0,123
0,006***
0,171
0,006***
0,199
0,007***
0,159
0,012***
0,174
0,013***
0,185
0,013***
0,144
0,012***
0,14
0,012***
0,203
0,015***
0,098
0,016***
0,099
0,017***
0,172
0,018***
0,116
0,011***
0,131
0,012***
0,175
0,013***
0,119
0,011***
0,14
0,012***
0,16
0,012***
0,126
0,009***
0,163
0,009***
0,172
0,01***
With bathtub
(yes=1)
-0,159
0,007***
-0,177
0,007***
-0,167
0,007***
-0,11
0,014***
-0,094
0,014***
-0,082
0,014***
-0,056
0,013***
-0,07
0,013***
-0,024
0,013*
-0,129
0,02***
-0,136
0,02***
-0,053
0,021**
-0,071
0,011***
-0,071
0,012***
-0,042
0,012***
-0,118
0,012***
-0,122
0,012***
-0,102
0,012***
-0,022
0,009**
-0,026
0,01***
-0,012
0,01
With built-in kitchen
(yes=1)
0,042
0,007***
-0,082
0,008***
0,044
0,013***
0,099
0,016***
-0,001
0,018
0,043
0,024*
0,018
0,014
-0,037
0,015**
0,071
0,019***
-0,011
0,022
-0,191
0,028***
0,062
0,043
0,096
0,013***
-0,022
0,014
0,162
0,019***
0,15
0,013***
0,037
0,015**
0,228
0,019***
-0,112
0,012***
-0,227
0,014***
-0,246
0,028***
With parking slot
(yes=1)
-0,112
0,008***
-0,159
0,008***
-0,039
0,011***
-0,073
0,018***
-0,114
0,018***
-0,057
0,02***
-0,085
0,016***
-0,101
0,017***
-0,05
0,018***
-0,045
0,025*
-0,097
0,026***
0,013
0,028
-0,063
0,013***
-0,109
0,014***
-0,021
0,016
-0,04
0,015***
-0,066
0,015***
0,026
0,017
-0,174
0,01***
-0,197
0,011***
-0,061
0,013***
With terrace
(yes=1)
-0,116
0,009***
-0,131
0,009***
0,011
0,012
-0,075
0,017***
-0,093
0,018***
-0,021
0,02
-0,113
0,016***
-0,147
0,016***
-0,011
0,021
-0,054
0,023**
-0,111
0,024***
0,088
0,032***
-0,074
0,015***
-0,144
0,015***
0,053
0,021**
-0,064
0,016***
-0,111
0,017***
0,109
0,024***
-0,049
0,011***
-0,042
0,012***
0,076
0,014***
With balcony
(yes=1)
-0,053
0,007***
-0,042
0,007***
-0,03
0,008***
-0,034
0,015**
-0,051
0,015***
-0,04
0,016**
-0,06
0,014***
-0,061
0,014***
-0,022
0,014
-0,044
0,022**
-0,086
0,023***
-0,009
0,025
-0,035
0,012***
-0,044
0,013***
-0,012
0,014
-0,028
0,013**
-0,061
0,014***
-0,03
0,015**
0,023
0,01**
-0,003
0,01
0,021
0,011*
With elevator
(yes=1)
0,019
0,007***
-0,05
0,008***
-0,063
0,009***
0,001
0,017
-0,059
0,018***
-0,035
0,019*
0,077
0,014***
0,011
0,015
0,035
0,016**
-0,055
0,027**
-0,129
0,028***
-0,066
0,03**
-0,083
0,013***
-0,132
0,014***
-0,151
0,014***
-0,046
0,013***
-0,117
0,014***
-0,04
0,016**
-0,064
0,011***
-0,094
0,012***
-0,162
0,013***
First occupancy
(yes=1)
-0,208
0,009***
-0,36
0,011***
0,097
0,03***
-0,144
0,019***
-0,215
0,019***
0,047
0,036
-0,236
0,017***
-0,374
0,019***
0,096
0,044**
-0,117
0,029***
-0,343
0,034***
0,297
0,077***
-0,16
0,019***
-0,336
0,02***
0,139
0,041***
-0,113
0,018***
-0,245
0,02***
0,235
0,042***
-0,171
0,013***
-0,234
0,014***
0,155
0,025***
Degree of atypicality
-0,266
0,007***
-3,571
0,29***
-0,23
0,015***
-1,904
0,493***
-0,279
0,017***
-1,955
0,622***
-0,407
0,031***
-2,765
0,821***
-0,329
0,014***
-3,221
0,492***
-0,236
0,015***
-4,246
0,5***
-0,243
0,012***
-3,749
0,351***
Degree of overpricing
-0,042
0,008***
-0,104
0,019***
0,024
0,016
-0,058
0,032*
0,054
0,018***
0,177
0,036***
0,049
0,024**
0,164
0,05***
0,077
0,011***
-0,058
0,03*
-0,034
0,015**
-0,019
0,022
-0,019
0,009**
-0,002
0,025
Centroid to ZIP
-0,019
0,001***
-0,011
0,012
0,003
0,005
0,067
0,024***
-0,021
0,004***
-0,006
0,022
0,022
0,007***
-0,04
0,029
-0,003
0,003
0,065
0,015***
-0,014
0,003***
0,016
0,019
-0,015
0,002***
0,027
0,013**
Centroid to NUTS3
-0,056
0,007***
-0,07
0,018***
-0,046
0,016***
-0,076
0,021***
0,004
0,008
-0,13
0,015***
-0,086
0,007***
Coordinates
Socioeconomic variables
Age fixed effects
ZIP fixed effects
Time fixed effects
Smoothing functions
Pseudo R² 61,9% 64,4% 65,9% 63,3% 64,3% 65,8% 65,3% 66,7% 68,1% 64,3% 66,2% 67,8% 60,9% 63,6% 65,3% 61,3% 62,6% 64,5% 64,9% 67,0% 68,2%
N 119’481 28’641 32’216 12’755 36’940 35’814 70’125
Notes:*Significant at the 10%-level; ** significant at the 5%-level; *** significant at the 1%-level. The exhibit shows the regression results of a semiparametric cox regression of dwellings’ time-on-market in weeks on hedonic, spatial, socioeconomic and smoothing covariates. The results are presented as coefficients, while significant positive
values shorten the survival and thus increase the assets’ liquidity, significant negative coefficients decrease the hazard rate and lengthen the survival. The three different model parameterizations control for different fixed effects. The Pseudo-R² based on Kendall’s Tau measures the concordance between estimated survival time and the observed
survival time for only the non-censored response sample.