House Prices at Different Stages of the Buying/Selling Process - - PowerPoint PPT Presentation

House Prices at Different Stages of the Buying/Selling Process

Presentation to the Ottawa Group Meeting 2011

• Presentation to the Ottawa Group Meeting 2011

in Wellington, New Zealand

Shimizu,C., K.Nishimura and T.Watanabe May 5, 2011

Purpose of the paper

Key research question

Are house prices different depending on the stages of the buying/selling process?

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Data

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Four prices from three datasets

Three datasets for the prices of condominiums traded in Tokyo, 200512009: %

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+

& +,)- .'+,)-.*

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Four prices: 1 ) 2 0 3 1 4 +

+,)- .'+,)-.*

+

&23+%3 )&&

PP% N4#567 N48#68 N4//!#9 :"N4!87#!

8

+ + P!N4/#9 P#N4/56#6 N49// N48!

Timeline of P1,P2,P3 and P4

• (

; <

• %

(P1)

%

(P2)

Timing of events in real estate transaction process Real estate price information

10 weeks

• 9
• (P2)

%"

1=

1 3++,)-. & +

• (P3)

& 3+ &" 3+ & >"

(P4) 15.5 weeks 5.5 weeks

Price distributions

Figure 3: Price densities for P1, P2, P3, and P4

7$/ 7$7 7$/

? ? ?!

5

7$77 7$7/ 7$7 #$/7 #$9/ /$77 /$/ /$/7 /$9/ 8$77 8$/ 8$/7 8$9/ 9$77 9$/ 9$/7 9$9/ 5$77 5$/ 5$/7 5$9/ 6$77 6$/ 6$/7 6$9/ 7$77 7$/ 7$/7

?! ?#

log P

Figure 4: Density functions for the house attributes : Floor Space

7$7 7$/ 7$!7

?@? ?!

6

7$77 7$7/ 7$7 7$/ 7 7 !7 #7 /7 87 97 57 67 77 7 7 !7 #7 /7 87 97 57 67 77 7 7 !7 #7 /7

?#

square meters

Empirical method

7

Two methods for quality adjustment 1. Intersection approach

A : " $& $ A & $ $

• 2. Quantile hedonic approach

A $& "2$ A &%%77/ %% 775

Results1:Intersection Approach

? 2

• 7$7

7$/

? ?#

0.20 0.25

? ?#

?

!

7$77 7$7/ 7$7 7$/ #$/ /$5 9$7 5$! 6$/

• 0.00

0.05 0.10 0.15 4.5 5.8 7.0 8.3 9.5

Figure 7. Quantile1Quantile Plot

?!vs ?# ?vs ?# + B

#

)C

• B

Results2:Hedonic Approach

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Quantile hedonic approach

• D

i i

Q p z z

θ

β θ =

• 7

θ ∈

θ

• D

F p z

θCth

8

i

β θ

• D

i

Q p z

θ

• D

i

F p z

Quantile hedonic approach

C7$77778 C7$7777# C7$7777 7 7$7 7$7/ 7$75 7$ 7$# 7$9 7$ 7$! 7$8 7$6 7$! 7$!/ 7$!5 7$# 7$## 7$#9 7$/ 7$/! 7$/8 7$/6 7$8 7$8/ 7$85 7$9 7$9# 7$99 7$5 7$5! 7$58 7$56 7$6 7$6/ 7$65

Distance to the nearest station

? ? ?!

(

9

C7$777 C7$7775 C7$7778 C7$777# C7$777 C7$777 C7$77775 ?# ;3.

Quantile hedonic approach:

8 8$ 8$# 8$8 8$5 9 9$ 9$# 9$8 9$5 5

Intercept

? ? ?! ?# 7$778 7$775 7$7 7$7 7$7# 7$78 7$75 7$7

Floor space

? ? ?! ?#

E

i b

β

5

8 7$7 7$7/ 7$75 7$ 7$# 7$9 7$ 7$! 7$8 7$6 7$! 7$!/ 7$!5 7$# 7$## 7$#9 7$/ 7$/! 7$/8 7$/6 7$8 7$8/ 7$85 7$9 7$9# 7$99 7$5 7$5! 7$58 7$56 7$6 7$6/ 7$65 7$7 7$7/ 7$75 7$ 7$# 7$9 7$ 7$! 7$8 7$6 7$! 7$!/ 7$!5 7$# 7$## 7$#9 7$/ 7$/! 7$/8 7$/6 7$8 7$8/ 7$85 7$9 7$9# 7$99 7$5 7$5! 7$58 7$56 7$6 7$6/ 7$65 C7$7!/ C7$7! C7$7/ C7$7 C7$7/ C7$7 C7$77/ 7 7$7 7$7/ 7$75 7$ 7$# 7$9 7$ 7$! 7$8 7$6 7$! 7$!/ 7$!5 7$# 7$## 7$#9 7$/ 7$/! 7$/8 7$/6 7$8 7$8/ 7$85 7$9 7$9# 7$99 7$5 7$5! 7$58 7$56 7$6 7$6/ 7$65

Age of building

? ? ?! ?# C7$777 C7$7775 C7$7778 C7$777# C7$777 C7$777 C7$77775 C7$77778 C7$7777# C7$7777 7 7$7 7$7/ 7$75 7$ 7$# 7$9 7$ 7$! 7$8 7$6 7$! 7$!/ 7$!5 7$# 7$## 7$#9 7$/ 7$/! 7$/8 7$/6 7$8 7$8/ 7$85 7$9 7$9# 7$99 7$5 7$5! 7$58 7$56 7$6 7$6/ 7$65

Distance to the nearest station

? ? ?! ?#

Differences between price distributions

( )

E

• D

i i

F p z p zβ θ = →

( ) ( )

• #

# # # #

E

• D

E

• D

P F p z p z P F p z p z β θ β θ = = → →

6

( )

# # # # #

E

• D

P F p z p z β θ = →

• #

#

• E

E

• D
• E

E

• D

F p F p z u z dz F p F p z u z dz

∞ −∞ ∞ −∞

≡ ∫ ≡ ∫

Decompose of distribution

A We calculate the distribution of :

( ) ( ) ( )

• ##

# #

E

• E
• E

b b

p z b p z b p z b β β β = = = ・ ・ ・

7

( )

#

• #

E

b

p z b β = ・

(a)Coefficient differences: (b)Variables differences: ↓ (a)+(b):Total differences:

• #

#

• #

#

• #
• #
• p

p p p p p − − −

(a) Coefficient differences

Coefficient differences:

B

• P

( ) ( )

#

• E

E

b b

b z z b β β − ・ ・ ・

• ( )
• E

β θ

( )

#

E β θ

• z

#

z

/7777

Variables differences

Variables differences:

B

• P#

( ) ( )

# # #

• E

E

b b

b z z b β β − ・ ・ ・

• ( )
• E

β θ

( )

#

E β θ

• z

#

z

/7777

Total differences

Total differences:

( ) ( )

• #

#

E E

b b

z b z b β β − ・ ・ ・

B

• PP#

!

( )

• E

β θ

( )

#

E β θ

• z

#

z

/7777

Figure 10: Decomposition of density differences: 1 . 4

7$7 7$/ 7$7 7$/

& F 1

#

C7$7 C7$/ C7$7 C7$7/ 7$77 7$7/ #$9 #$6 /$ /$ /$# /$8 /$5 8$7 8$ 8$! 8$/ 8$9 8$6 9$7 9$ 9$# 9$8 9$5 9$6 5$ 5$! 5$/ 5$9 5$5 6$7 6$ 6$# 6$8 6$9 6$6 7$ 7$! 7$/ 7$8 7$5 $7 $ $# $/

1

Figure 10: Decomposition of density differences: P2 . P4

7$7 7$/ 7$7 7$/

& F 1

/

C7$7 C7$/ C7$7 C7$7/ 7$77 7$7/ #$9 #$6 /$ /$ /$# /$8 /$5 8$7 8$ 8$! 8$/ 8$9 8$6 9$7 9$ 9$# 9$8 9$5 9$6 5$ 5$! 5$/ 5$9 5$5 6$7 6$ 6$# 6$8 6$9 6$6 7$ 7$! 7$/ 7$8 7$5 $7 $ $# $/

Figure 10: Decomposition of density differences: P3 . P4

7$7 7$/ 7$7 7$/

& F 1

8

C7$7 C7$/ C7$7 C7$7/ 7$77 7$7/ #$9 #$6 /$ /$ /$# /$8 /$5 8$7 8$ 8$! 8$/ 8$9 8$6 9$7 9$ 9$# 9$8 9$5 9$6 5$ 5$! 5$/ 5$9 5$5 6$7 6$ 6$# 6$8 6$9 6$6 7$ 7$! 7$/ 7$8 7$5 $7 $ $# $/

Figure 7c. Quantile1Quantile Plot for Quality Adjusted Prices ?vs ?# ?vs ?#

9

?!vs ?#

Main findings

$ &= $ $ ( " $ $ !$ &

• =

2 $

5

Additional question

An important question to be asked is whether the deviations differ depending on whether the housing market is in a downturn or in an upturn ?

• & P P

&" P " P "$

6

Hedonic Indices of 1 and 2

7$/ 7$7 7$/

(=? (=?

!7 7$77 7$7/ 7$7 77/79 77/7 7787 7787# 77879 7787 7797 7797# 77979 7797 7757 7757# 77579 7757 7767 7767# 77679 7767

Price Ratio between 1 and 2

7$69/ 7$657 7$65/ 7$667 7$66/

ice ratio !

7$6/7 7$6// 7$687 7$68/ 7$697 200507 200510 200601 200604 200607 200610 200701 200704 200707 200710 200801 200804 200807 200810 200901 200904 200907 200910

Price rat

?3

Interval between 1 and 2

!/ #7 #/ /7 // 87 7$697 7$69/ 7$657 7$65/ 7$667 7$66/

nterval [days] rice ratio !

87 8/ 97 9/ 57 7$6/7 7$6// 7$687 7$68/ 7$697 200507 200510 200601 200604 200607 200610 200701 200704 200707 200710 200801 200804 200807 200810 200901 200904 200907 200910

Interva Price ra

?3 )"+)"

Additional findings

$ =? $ $ & =P =P February 2009two months earlier =P$ !$ &" G" four months before =P $

!!

?# ?!

!#

? ?

Source: National Statistician’s Review of House Price Statistics,UK2010

Figure 2: Intervals between events in the house buying/selling process

7$#777 7$/777 7$8777 & ?? & ??! & ??#

!/

7$7777 7$777 7$777 7$!777 7$#777 7 /7 77 /7 77 /7 !77 !/7 #77 #/7 /77 //7 877 8/7 977 9/7 577 5/7 677 6/7 777

days

Timeline of P1,P2,P3 and P4

• (

; <

• %

(P1)

%

(P2)

Timing of events in real estate transaction process Real estate price information

10 weeks

• !8

!8

• (P2)

%"

1=

1 3++,)-. & +

• (P3)

& 3+ &" 3+ & >"

(P4) 15.5 weeks 5.5 weeks

Figure 5: Densities for relative prices

!9

Figure 4: Density functions for the house attributes: Building Age

7$/ 7$7 7$/

?@? ?!

!5

7$77 7$7/ 7$7 7$/ 7 / 7 / 7 / !7 !/ #7 #/ /7 // 87 8/

?! ?#

years

Quality adjustment

• #

#

• D
• D

F p F p z u z dz F p F p z u z dz

∞ −∞ ∞ −∞

= ∫ = ∫

!6

[ ] [ ]

• #
• #
• #
• #
• D
• D
• D

F p F p F p z F p z u z dz F p z u z u z dz

∞ −∞ ∞ −∞

− = − ∫ + − ∫

↓

Kolmogorov1Smirnov test

1statistic 1value Number of observations

1 vs. 4 0.2016 0.000 155,347 for 1 and 58,949 for 4 2 vs. 4 0.1885 0.000 155,347 for 2 and 58,949 for 4 vs. 0.0432 0.000 122,547 for and 58,949 for

• #7

3 vs. 4 0.0432 0.000 122,547 for 3 and 58,949 for 4 1 vs. 4 0.0584 0.000 14,890 for 1 and 14,890 for 4 2 vs. 4 0.0441 0.000 14,890 for 2 and 14,890 for 4 3 vs. 4 0.0303 0.000 22,613 for 3 and 22,613 for 4

! !

Table 4: Goodness1of1Fit Tests

1 1value Number of observations 1 vs. 4 0.2016 0.000 155,347 for 1 and 2 vs. 4 0.1885 0.000 155,347 for 2 and 3 vs. 4 0.0432 0.000 122,547 for 3 and

• ! !

#

1 vs. 4 0.0584 0.000 14,890 for 1 and 14,890 2 vs. 4 0.0441 0.000 14,890 for 2 and 14,890 3 vs. 4 0.0303 0.000 26,496 for 3 and 26,496 1 vs. 4 0.0676 0.000 50,000 for 1 and 50,000 2 vs. 4 0.0535 0.000 50,000 for 2 and 50,000 3 vs. 4 0.0199 0.000 50,000 for 3 and 50,000 " ! !