The document discusses rent prediction models using floor plan images, detailing various property attributes that affect rental prices such as location, age, and building structure. It compares the accuracy of different prediction models, including linear regression and support vector regression, in relation to using floor plan images. The study aims to validate the impact of these images on rental price predictions, highlighting the custom in Japan of examining floor plan layouts during property searches.

1. / 20
IEEE GCCE 2019
2019.10.16
Rent Prediction Models with Floor Plan Images
1
Ryosuke Hattori¹ Kazushi Okamoto¹ Atushi Shibata²
¹ Graduate School of Informatics and Engineering，The University of Electro Communications
² Graduate School of Industrial Technology，Advanced Institute of Industrial Technology

2. / 20
IEEE GCCE 2019
2019.10.16
Features of properties
• almost properties with different attributes
• property attributes
• age, story, building structure, and so on
• effect on prices
Rental case comparison method (Onoki，2016)
• referring around the target property and the similar properties
• linear regression model
Introduction
2
K. Ohno: Keizoku Chinryou Kantei Hyouka Wo Saikou
Suru, Jutaku-Shimpo Inc., 2016
ŷi = ↵1x1 + ↵2x2 + ↵3x3 + · · · + ↵nxn +
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rent distance
story
age location
ŷ
<latexit sha1_base64="xhpVBugrKGqsl/IOdSNq+waH9Pk=">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</latexit>

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IEEE GCCE 2019
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Related Works
3
• R P. Dahal, R K. Grala, J S. Gordon, L A. Munn, D R. Petrolia, J R. Cummings: A hedonic pricing method to estimate the value of waterfronts
in the gulf of Mexico, Urban forestry & urban greening, vol.41, pp.184-194, 2019.
• P. Deschermeier, B. Seipelt: A hedonic rent index for student housing in Germany, Cologne institute for economic research, pp.1–12, 2016.
• Y. Jun, H. Kim: Measuring the effect of greenbelt proximity on apartment rents in Seoul, Cities, vol. 62, 2017.
category of explanatory variable
building
structure
location
/access
around
environment
sales
contract
image
Ram+
2019
✔︎✔︎✔︎
Philipp+
2016
✔︎✔︎✔︎
Jun+
2017
✔︎✔︎✔︎✔︎
proposal
✔︎✔︎✔︎✔︎

4. / 20
IEEE GCCE 2019
2019.10.16
Floor Plan Standard
4
kitchen and
other rooms
kitchen square
[0 m2,4.5 m2) [4.5 m2, 8.0 m2)
More than equal to
8.0 m2
not
separated
Room
(R)
separated
Kitchen
(K)
Dinning Kitchen
(DK)
Living Dinning Kitchen
(LDK)
DK LDK
K
R

5. / 20
IEEE GCCE 2019
2019.10.16
Floor Plan Images
Floor plan standard is the same, but
fl
oor layout is different
In japan, there is a custom to look at
fl
oor plan images
when searching for a desired rental property (Kiyota+，2017)
5
Ex. Different
fl
oor plan images in the same property and
fl
oor standard
Y. Kiyota, T. Yamasaki, H. Suwa, C. Shimizu:
Real estate and AI, Journal of the Japanese
Society for Arti
fi
cial Intelligence, vol.32, no.4,
pp.529-535, 2017

6. / 20
IEEE GCCE 2019
2019.10.16
Purpose: Validate in
fl
uence of
fl
oor plan images to rent prediction
Method
• build prediction models with/ without
fl
oor plan images
• compare prediction accuracy
Prediction Models
• Linear Regression (LR)
• XGBoost
• Support Vector Regression (SVR)
Feature Extractor
• Principal Component Analysis (PCA)
6
Our Approach

7. / 20
IEEE GCCE 2019
2019.10.16
Prediction formula：
Loss function :
Prediction Model
7
ln
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L(y, f(x)) =
X
(ln yi ln ŷi)2
=
X
(ln
yi
ŷi
)2
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ln ŷ = f(x) = ↵T
x +
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u
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<latexit sha1_base64="x42OIFZO63bltL4H6gjza3ReZFg=">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</latexit>
vi
ln ŷ = f(x) = ↵T
x +
<latexit sha1_base64="P9YN1Afnk0PPWLWhmqBsXdXQnxM=">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</latexit>
x = [u, ✓(v)]
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特
徴
量
属性
ベクトル
賃料
Image
Features
Property
Variables
Rent
ln ŷ = f(x) =
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✓(v)
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<latexit sha1_base64="2CcQ3v4PnGrVgMig/t6jEP2FmtA=">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</latexit>
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<latexit sha1_base64="yBk8zvv0E/O9omSla+R64kZVUTs=">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</latexit>
<latexit sha1_base64="yBk8zvv0E/O9omSla+R64kZVUTs=">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</latexit>
<latexit sha1_base64="yBk8zvv0E/O9omSla+R64kZVUTs=">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</latexit>
<latexit sha1_base64="yBk8zvv0E/O9omSla+R64kZVUTs=">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</latexit>
<latexit sha1_base64="yBk8zvv0E/O9omSla+R64kZVUTs=">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</latexit>
<latexit sha1_base64="yBk8zvv0E/O9omSla+R64kZVUTs=">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</latexit>

8. / 20
IEEE GCCE 2019
2019.10.16
LR1
XGBoost2
• max depth: 6, 8, 10
SVR3
Used Regressors
8
ln
<latexit sha1_base64="cip2YhSnDwuYWh3CEG2B1EDrAqU=">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</latexit>
ln ŷ = wT
h(x) + c
<latexit sha1_base64="9NdTbvfNygiSlMxs/yuuDlK+3Ew=">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</latexit>
<latexit sha1_base64="9NdTbvfNygiSlMxs/yuuDlK+3Ew=">AAACoXichVFNLyxBFD0aD8N7BhuJTceEkJdIjUiIkAgbdmMYJNPepLvVmI7qj3TXDKPTf8AfsLAiEZH3M97GlsTCTxBLEhsLd3o6EcRzK1X31Ln33LpVZXjCCiRjdy1Ka1v7j47OrlR3z89fvem+/o3ArfomL5iucP0tQw+4sBxekJYUfMvzuW4bgm8ae0uN+GaN+4HlOuuy7vFtW991rLJl6pKoUnpOE06oVXQZ1qNInQ81wxU7Qd0mp+5Hf0LNt9X1SK2MvYscROPqb9UspTNsgsWmfgbZBGSQWM5NX0DDDlyYqMIGhwNJWEBHQKOILBg84rYREucTsuI4R4QUaauUxSlDJ3aP1l3aFRPWoX2jZhCrTTpF0PRJqWKE3bJL9siu2F92z16+rBXGNRq91MkbTS33Sr1Hg2vP36ps8hKVN9V/e5YoYybu1aLevZhp3MJs6muHx49rs/mRcJSdsQfq/5TdsX90A6f2ZJ6v8vwJUvQB2Y/P/RlsTE5kCa9OZRYWk6/oxBCGMUbvPY0FLCOHAp17hitc40bJKCtKTsk3U5WWRDOAd6YUXwHgWaGG</latexit>
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<latexit sha1_base64="9NdTbvfNygiSlMxs/yuuDlK+3Ew=">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</latexit>
1Linear regression class of scikit-learn (version: 0.20.2)
2https://github.com/dmlc/xgboost of python package (version: 0.82)
3SVR class of scikit-learn (version: 0.20.2)
F = {f(x) = wq(x)}
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ln ŷ =
K
X
k=1
fk(xi), fk 2 F,
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ln ŷ = ↵T
x +
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9. / 20
IEEE GCCE 2019
2019.10.16
Experiment Environment
LIFULL HOME ’S dataset (as of September 2015)
• rental property data (70 variables, 5.33 million)
• 120 x 120 pixel image data (83 million
fi
les)
• used data
• Tokyo
• 1,089,090 properties
• 34 property variables
•
fl
oor plan image
Computer (OS: Ubuntu 18.04.1)
• CPU：Xeon(R) CPU E5-2650 v3 @ 2.30GHz
• RAM：64GB
9

10. / 20
IEEE GCCE 2019
2019.10.16 10
Dataset (1/2)
building structure/sales construct (19 variables)
property type parking status
num. of ground
fl
oors
num. of
underground
fl
oors
fl
oor num.
property status
available num. of
parking
num. of rooms building structure
orientation of
property
purpose of use
total num. of
properties
keeper exclusive area
num. of vacant
properties
age of building
immediate
occupancy
contract period days of occupancy
the others: one-hot vector
: continuous variables

11. / 20
IEEE GCCE 2019
2019.10.16 11
Dataset (2/2)
location/acces (15 variables)
city, ward, town city plan rail station 1 rail station 2 area description
dist. to general
hospital
dist. to parking
walking dist. to
station 1
or bus stop
walking dist. to
station 2
or bus stop
dist. to
convenience store
dist. to super
market
dist. to junior high
school
riding dist. to
station 1
riding dist. to
station 2
dist. to elementary
school
the others: one-hot vector
: categorical each 80m
grayscale
Floor plan images

12. / 20
IEEE GCCE 2019
2019.10.16
Outline of Experiment
12
1. Dataset split
• divide used dataset into 4 standards: K, R, DK, LDK
• for each standard, divide 20% into the test dataset
and 80% into the development dataset
2. Hyper-parameter tuning
• apply hold-out method to the development datasets
• evaluation measure: Mean Squared Error (MSE)
3. Evaluation of LR, XGBoost, and SVR for the test datasets
• MSE and learning time
• visualize predicted values
MSE =
1
N
X
(ln
yi
ŷi
)2
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13. / 20
IEEE GCCE 2019
2019.10.16
Suitable Hyper-parameter for Each Model
13
fl
oor plan standard
hyper
parameter
K R DK LDK
LR
regularization
method
lasso lasso lasso lasso
lambda 0 0.001 0 0.001
PCA 512 2048 1024 2048
XGBoost
max depth 10 10 10 10
PCA 512 256 1024 1024
SVR
epsilon 0.1 0.01 0.1 0.01
PCA 256 256 512 256

14. / 20
IEEE GCCE 2019
2019.10.16
Learning Time for the Training Dataset K
14
learning
time
[sec]

15. / 20
IEEE GCCE 2019
2019.10.16
MSE for the Test Datasets
15
# of data
LR XGboost SVR
w/ w/o w/ w/o w/ w/o
K 30,446 0.028 0.029 0.015 0.016 0.159 0.161
R 94,420 0.024 0.029 0.013 0.014 0.099 0.100
DK 35,405 0.018 0.027 0.010 0.017 0.086 0.086
LDK 55,039 0.030 0.030 0.010 0.018 0.151 0.151

16. Predicted Values for the Test Dataset K
XGBoost w/o
XGBoost w/
LR w/o
LR w/ SVR w/
SVR w/o

17. Predicted Values for the Test Dataset DK
XGBoost w/o
XGBoost w/
LR w/o
LR w/ SVR w/
SVR w/o

18. / 20
IEEE GCCE 2019
2019.10.16
Cumulative Density (1/2)
18
K [30,446 properties] R [94,420 properties]
= |1 −
̂
y
y
|
error rate y : ground truth ̂
y : prediction
ratio
the
number
of
data
error rate

19. / 20
IEEE GCCE 2019
2019.10.16
DK [35,405 properties] LDK [55,039 properties]
19
Cumulative Density (2/2)
= |1 −
̂
y
y
|
error rate y : ground truth ̂
y : prediction

20. / 20
IEEE GCCE 2019
2019.10.16
Purpose: Validate in
fl
uence of
fl
oor plan images to rent prediction
Method
• build prediction models with/ without
fl
oor plan images
• compare prediction accuracy
Result
• Floor plan images reduce prediction variability
• XGBoost with
fl
oor plan images archives the best MSE
Future work: To apply Speeded-Up Robust Features (SURF)
20
Conclusion