2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)

Predicting inquiry from potential renters using
property listing information
Takeshi SO
Daito Trust Construction Co.,LtdInstitute of Future DesigninHousing Market
Minato ward, Tokyo, Japan
st102456@kentaku.co.jp

Yuta Arai
Reitaku University
AI Business Research Center
Kashwa City, Chiba Prefecture,Japanyuarai20@reitaku-u.ac.jp

positively when new instead of traditional statistical methods areused.

Abstract—In this study, we deduced how accurate the numberof inquiries from potential tenants for housing available for rentcan be predicted based on the attributes of the housing, using
multiple statistical methods, and compared the results. The
purpose of this study is to show these results as case studies.
Confusion matrices were calculated based on the results deducedwith three methods – the classical logistic regression,
RandomForest, and XGBoost – and prediction accuracies wereverified. The results showed that the accuracy of XGBoost was thehighest, followed by that of logistic regression. It is sometimes
desirable to use logistic regression, which is easy to interpret fromthe perspective of application to business, because the differencesin accuracy among the statistical methods are not significant. It isthus important in business to take into account the accuracy, easeof interpretation, and research structure and select the mostappropriate statistical method.

The background reason for this is that, for a single problem
to be solved, the results from multiple functions are beingcompared on an ad hoc basis, with insufficient practical casestudies available for reference. Most of the reports from appliedresearch simply present the data for the problem at hand, themethods (functions) applied, and the results. Very few reports
have described why they used certain methods, and how theirresults would have differed if they had used other methods.
Thus, the current study uses three methods (logisticregression, XGBoost[8], and RandomForest[9]) to derive
estimates for the following question: “Can property listing
information be used to determine/predict whether potential
renters will make inquiries concerning specific listed properties?”
Added to this are considerations regarding differences inaccuracy among the three methods, and which applications areeasiest to apply in actual business, and so on. The purpose is toprovide practical research that can be used as a case study.

Keywords—Housing market, Response Analysis, Property
Equipment, Machine Learning, Logistic Regression

I. RESEARCH BACKGROUND AND PURPOSE

Accumulation of such case studies can contribute to theselection of appropriate methods for use in practical analyses
regarding analytical structure including verification tasks andstatus, the skills and experience of technical personnel, thenumber of persons for analysis, and so on.

Statistical methods used for data analysis are constantly
improving, with numerous researchers developing newalgorithms, with novel implementations (functions) being added
to statistical software and computer languages.
The more functions implemented, the greater the confusion
as to which are best for actual usage. Depending on the purposeof the solution task, general divisions of usable functions exist,
including discrete variable estimation, discrimination, variable
aggregation, and so on. Nevertheless, the actual selection ofappropriate functions often depends on the experiences andstrengths and weaknesses of those performing the analysis. Inthe business field, the trend indicates that clients respond

II. PREVIOUS RESEARCH
A related study regarding the current research question isreported [1]1.
Other studies using predictive models have been conductedin fields other than real estate, for example, reference [2] study
on reservation rates for accommodation facilities (hotels, inns,etc.). Predictive model-related research is conducted by a variety

1

The process before moving into a rental residence can be divided into two broad types. One is where a potential renter firstconducts a search by themselves until they find a property suitable for inquiry. The other method is to visit a real estate agent,
where detailed information is provided. The potential renter then inspects the property and decides whether to rent it. In the latterprocess, elements other than advertised information become important. It is therefore thought that it is not enough to use only
property information as the explanatory (predictor) variable in model construction.

978-1-6654-1865-2/21/$31.00 ©2021 IEEE
DOI 10.1109/MIPR51284.2021.00053

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of private firms for revenue management, such as whencompanies seek to maximize sales using unit prices andprediction rates. There are many large corporations in theaccommodation sector, and these firms have access to the large
amounts of data required for analysis. However, conditions andstatus in the real estate sector differ greatly by country. In Japan,
there are almost no real estate companies that have the large
amount of data required for robust analyses. This is one major
reason for the relative lack of related research.

Table 1: Descriptive statistics of property data
Area AgeRent(× 䢠 10,000) (䟝) (years)

March
2019

Much analysis in the real estate sector has focused onestimating rent and prices for condominiums for resale[3-6].
However, classical multiple regression analysis was used in allof these studies, with no comparison made among multiple
statistical methods2.

June
2019

N
Mean
SD
MinMaxN
MeanSDMin
Max

8.82 24.02
2.86 3.26
2.00 15.00
22.60 30.00
8.80 24.27
2.75 3.23
2.50 15.00
22.60 30.00

Walking distancefrom railway(minutes)

34,736
8.70
8.42
0.00
30.00
15,641
8.60
8.00
0.00
30.00

7.06
3.59
1.00
15.00
6.57
3.37
0.00
15.00

and walking distance to the nearest train station was 15 minutes
or less.

Reference [7] compared multiple methods, comparing error
rates of traditional regression analysis, a neural network, and aregression tree. This study was rich in suggestions, including
considerations regarding nonlinear and linear models, and so on.

Descriptive statistics of properties having responses (i.e.,inquiries) are reported in Table 2.

Nonetheless, there is insufficient research performed withadded considerations from the practical perspective of which
methods are most easily applied in actual business practice.

Comparing Tables 1 and 2, only approximately 2% of allproperties had responses, with the majority of listings having noresponses4.

III. RESEARCH METHOD

Focusing on the descriptive statistics, for properties withresponses, the rent was around ¥20,000 lower, and years after
construction was around 1.5 years more for properties withresponses. Meanwhile, no large differences (deviations) wereobserved in the other attributes, floor area, and distance fromstation. This shows that it is difficult to predict whether a
specific property will generate a response based on fundamental
items such as floor area and so on.

3.1 Data
The present study analyzed rental properties for sale (i.e.,listed) from real estate agencies specializing in rentals3 in theTokyo Metropolitan area and in the three prefectures of Saitama,
Chiba, and Kanagawa. In Japan’s rental housing market, January
through March is considered a peak period, especially in termsof inquiries for rental property visitations (“previews”).
Therefore, this study selected two time points, March 2019
during the peak period, and June 2019, during off-peak period.
For these time points, we analyzed which kind of property
attributes were included in email inquiries (“responses”). To
reduce heterogeneity in property attributes, floor area waslimited to 15 to 30 m2 for non-married potential renters.

Table 2: Descriptive statistics of properties having responses
Walking distancefrom railway(minutes)
682䠄response rates䠖1.96%䠅
7.02 23.93 10.28
7.19
2.23 3.66
9.12
3.58
2.60 15.01
0.00
1.00
19.80 30.00 30.00
15.00
442䠖(response rates䠖2.82%)
7.90 23.73
8.31
6.99
2.45 3.60
7.97
3.53
2.58 15.65
0.00
1.00
16.40 30.00 30.00
15.00

Area AgeRent(× 䢠10,000) (䟝) (years)

March2019

Descriptive statistics for the property data used in this study
are reported in Table 1.
The number of properties listed within the target area in therespective one-month periods was approximately 350,000
properties for March 2019 and 160,000 for June 2019. To
eliminate abnormal values, target rents were set at ¥20,000
through ¥300,000, years since construction were 30 years or less,
2June
2019

NMeanSDMinMaxNMeanSDMinMax

SUUMO, LIFULL HOME’S, and athome are private services that provide rental market prices usable by anyone. LIFULLHOME’S has a service called PriceMap, where a user can browse a map with pre-owned condominiums/apartments listed withestimated prices (values). TERMINAL Inc., a real-estate tech venture, offers the commercial service, Sumasate, a rent appraisalservice. SRE Real Estate (formerly Sony Real Estate Corporation) also offers a price-appraisal service for pre-owned
condominiums/apartments. However, none of these services has published sufficient information regarding what methods are used
for what data for modelling.
3
Listed (offered) properties are not only those of the specific company, they also include properties managed by other companies(so-called “futures properties”).
4
As for actual concluded contracts, these are determined not only by email responses by specific websites. Many other routes exist,
including telephone inquiries, direct visits to agencies, referrals from other companies, and so on. Furthermore, since multiple realestate companies simultaneously list the same properties, the number of responses per property tends to become much lower.

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Therefore, this study used the aforementioned three methods.
For these, common explanatory variables were used in trials to
predict response rates, and the respective accuracy of eachmethod was compared.
It is generally known that there is a trade-off between model
estimation accuracy and explainability. Therefore, we usedlogistic regression analysis as a method with high explainability,and RandomForest and XGBoost as methods with good
estimation accuracy.

peak period of June 2019, estimations were made separately fordata from each of these. For the verification, data were divided
in an 8:2 ratio according to whether a response occurred, with 8
used as training data and 2 as data for tests. Further, since there
were very few “yes” response data items, the training data wasundersampled, while response probability was calculated for thetest data. This trial was performed 100 times for each method,
and a confusion matrix was calculated using the response
probability obtained for each property. This confusion method
was then used for assessments.

3.2 Variables
The explanatory variables used in this study are presented inTable 3. These include three shown in the descriptive statistics,
namely, floor area, years after construction, and time to walk tostation. Facilities and equipment variables, and lump sum (onetime) payments such as key money and security deposit, and soon were also added, among others. Property attributes including
property type and structure were categorical values, while
facilities and equipment were 0, 1 dummy variables. Dummy
variables were also created from discrete variables includingfloor area, years after construction, and so on, and analysis wasperformed accordingly5.

IV. RESULTS
For March 2019, the distribution of estimated probability
rates vis-à-vis properties which actually received responses isshown in Fig. 1.
Fig. 1 shows that, depending on the method used, there weremajor differences in the distribution of estimated response
probabilities. For example, median values were 0.14 for thelogistic regression, 0.58 for XGBoost, and 0.12 for
RandomForest. Distribution shapes that differ so widely indicatethat it is not appropriate to set common thresholds for response
probabilities.

Moreover, deviation from market rental price was used as aspecial variable. This shows the deviation between estimatedprices (rates) as calculated from data for analysis, and actual
listed rental prices. Strictly speaking, while it is not desirable toinsert estimated values into explanatory variables, in the case as
in recent years where consumers can view multiple properties
on Internet sites, consumers have acquired a certain sensibility
such that they can recognize when a property is priced somewhathigh or low. This is thought to make properties somewhat
cheaper than market rental rates more susceptible to a response.
Such deviation was therefore used as one explanatory variable.

Therefore, in the present study, the median value of response
probabilities vis-à-vis properties that actually received
responses was used as the threshold. The confusion matrix wascalculated as shown in Table 4, and estimation accuracy wasexamined.

3.3 Verification procedures
As there may have been a difference between responses that
occurred in the peak period of March 2019 and those in the offTable 3: List of explanatory variables
Variable attributes
Property attributes variables
Quantitative variales

Facilities and euipment variables

Lump sum variables
Address
Custom variables

Variable names
Property typeStructure
Area
Walk to station (time)Age
Elevator
Postal delivery box
Automatic lock
Heater toilet seat
Floor plan
First floor
Key money
Security deposit
City, ward, country
Deviation from market rent priceFig. 1 Distribution of estimated response rate

5As for rental housing, it is known that years after construction and floor space have nonlinear effects on rent. In the analysis,
dummies were created for these discrete variables, and thus they are not treated as linear.

285them when needed. This does not mean that use of the latest
methods is necessarily the best choice. For business applications,multiple methods should be tested and the statistical method to
be used should be selected while considering the proper balance
between accuracy rate and explanatory power (interpretability).
One must also always be aware of the risk of overfitting,depending on statistical method used.

Table 4: Confusion matrixMarch2019

June2019

MethodLogisticThreshold䠖0.13833
XGBoostThreshold䠖0.57917
RandomForest
Threshold䠖0.11509
LogisticThreshold䠖0.13114
XGBoostThreshold䠖0.536389
RandomForest
Threshold䠖0.10777

No response
Response
No response
Response
No response
Response
No response
Response
No response
Response
No response
Response

Estimated Estimatedno response response
77.00%
21.07%
0.97%
0.97%
78.56%
19.51%
0.97%
0.97%
67.40%
30.67%
0.97%
0.97%
74.63%
22.56%
1.41%
1.41%
73.37%
23.81%
1.41%
1.41%
63.36%
33.83%
1.41%
1.41%

A future issue is investigation of what differences occur inresults depending on the statistical method used after data havebeen appropriately stratified. One assumes that different
stratification approaches have differing results. Thus, there may
be a possibility that the appropriate stratification method may
depend on what statistical methods are to be used.

REFERENCES
Here, when the focus is on the detection rate where there wasno response both for actual properties and in the estimates6, it
can be seen that these were roughly the same for the logistic
regression and for XGBoost. While XGBoost shows a higher
detection rate for March 2019, the logistic regression shows ahigh detection rate for June 2019. In reality, the peak period ofMarch 2019 was of greater importance, as there were more listed
properties and more concluded contracts. In this sense, it can beargued that XGBoost had a somewhat higher accuracy than thelogistic regression.

[1]

[2]

[3]

In general, in classification problems of this nature, it isknown that RandomForest and XGBoost are able to makeclassifications of high accuracy. However, when thresholds areadjusted, the traditional logistic regression method can beconsidered to show sufficient classification accuracy forpractical purposes.

[4]

[5]

[6]

Ⅴ. Considerations and future issues
The present study compared the results of three different
methods: logistic regression, XGBoost, and RandomForest. In
this case, the use of classic logistic regression was considereddesirable for actual business applications.

[7]

[8]
[9]

When logistic regression and XGBoost have mostly thesame prediction accuracies, logistic regression results are easier
to interpret. With logistic regression, it is easier forbusinesspersons to understand reasons for using estimation
results to make decisions.

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vacancy rates within a locality”, Journal of the Japan Association for RealEstate Sciences, 32(1), 106-113, 2018.(in Japan)
T. So and C. Shimizu, “Housing Facilities and Housing Rent, Purchasing
and Supply Management”, (Peer review book), ISBN 978-1-78984-9738, 2019.
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T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system”,
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L. Breiman, “Random Forest”, Machine Learning 45, pp.5-32, 2001.

Certainly, there are some cases even in business when, in theuse of statistical methods, accuracy takes priority when there isno need for explanatory power or interpretability. However, if
accuracy rates are roughly equivalent, the logistic method iseasier to interpret and understand. In business sites where thestatistical results are to be used, it is easier to form a consensus
using logistic results. This in turn reinforces its usage in businessand is also thought to be connected with model refinement.
When engaging in model development, it is essential to havea grasp of the latest statistical methods, so as to be able to use
6

In actual business, business efficiency is improved when emphasis is placed on selling properties having a high likelihood ofresponse. The merits of this approach are therefore high.

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