log *pit* ¼ *β*<sup>0</sup> þ *β*<sup>1</sup> � *Ageit* þ ∑

þ ∑ 5 *k*¼1

þ ∑ 20 *n*¼1

is a constant term, and *εit* is an error term.

*<sup>j</sup> LCj* þ ∑

*k βST*

*β*6*<sup>k</sup>* � *Strkit* þ ∑

*β*9*<sup>n</sup>* � *REmit* þ ∑

A general hedonic model can be expressed as

### **3.1 Descriptive statistics for analysis data**

Before analysis, we calculated the descriptive statistics of the data to be analyzed (**Table 1**). From the descriptive statistics, there are several features as follows, depending on the period of construction:


### **Table 1.** *Descriptive statistics.*

• There is little difference between old and main stocks in average rent, but it is about 20% higher for new stock than the old stock.

**Number of observations Ratio**

**Total Old stock**

43.943 3447 12.851 27.645 82.1% 70.8% 64.3% 96.3% 25.5%

26.292 1412 6221 18.659 49.1% 29.0% 31.1% 65.0% 36.0%

12.678 364 1764 10.550 23.7% 7.5% 8.8% 36.8% 29.3%

27.307 2085 10.056 15.166 51.0% 42.8% 50.3% 52.8% 10.0%

26.042 337 5062 20.643 48.6% 6.9% 25.3% 71.9% 65.0%

6670 130 728 5812 12.5% 2.7% 3.6% 20.3% 17.6%

**Main stock**

**New stock** **New-old**

**New stock**

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing…*

Air conditioning 49.088 4029 17.883 27.176 91.7% 82.8% 89.5% 94.7% 11.9% Hot water supply 44.841 3879 16.961 24.001 83.7% 79.7% 84.9% 83.6% 3.9% Indoor WM area 43.954 2663 14.696 26.595 82.1% 54.7% 73.5% 92.7% 38.0%

Flooring 43.269 3364 14.915 24.990 80.8% 69.1% 74.6% 87.1% 18.0% Balcony 40.851 3204 15.276 22.371 76.3% 65.8% 76.4% 77.9% 12.1% System kitchen 27.758 1093 5666 20.999 51.8% 22.5% 28.4% 73.2% 50.7%

1 gas stove 25.300 1416 6396 17.488 47.2% 29.1% 32.0% 60.9% 31.8% Washlet 23.221 1089 3265 18.867 43.4% 22.4% 16.3% 65.7% 43.4% Bathroom dryer 20.322 186 1077 19.059 37.9% 3.8% 5.4% 66.4% 62.6% 2 gas stoves 18.632 1081 3304 14.247 34.8% 22.2% 16.5% 49.6% 27.4% Reheating bath 15.127 1268 3459 10.400 28.2% 26.0% 17.3% 36.2% 10.2%

Own house rental 7187 497 1588 5102 13.4% 10.2% 7.9% 17.8% 7.6% IH stovetop 6623 215 2653 3755 12.4% 4.4% 13.3% 13.1% 8.7% Walk-in closet 3694 88 235 3371 6.9% 1.8% 1.2% 11.7% 9.9% Counter kitchen 3409 70 516 2823 6.4% 1.4% 2.6% 9.8% 8.4% With loft 2110 19 754 1337 3.9% 0.4% 3.8% 4.7% 4.3% Underfloor heating 1147 8 87 1052 2.1% 0.2% 0.4% 3.7% 3.5%

Bicycle parking lot 33.795 2096 11.385 20.314 63.1% 43.1% 57.0% 70.8% 27.7%

TV intercom 26.689 953 4232 21.504 49.8% 19.6% 21.2% 74.9% 55.4%

Cable TV 23.211 1316 8314 13.581 43.3% 27.0% 41.6% 47.3% 20.3% BS antenna 20.013 472 4430 15.111 37.4% 9.7% 22.2% 52.7% 43.0% Elevator 19.587 1189 5387 13.011 36.6% 24.4% 27.0% 45.3% 20.9% Tiling wall 15.751 561 5265 9925 29.4% 11.5% 26.3% 34.6% 23.1% Delivery locker 15.163 119 1550 13.494 28.3% 2.4% 7.8% 47.0% 44.6% Security camera 12.694 302 1849 10.543 23.7% 6.2% 9.3% 36.7% 30.5% CS antenna 11.888 304 1837 9747 22.2% 6.2% 9.2% 34.0% 27.7%

Bike parking lot 6335 354 1875 4106 11.8% 7.3% 9.4% 14.3% 7.0%

**Item Total Old**

*DOI: http://dx.doi.org/10.5772/intechopen.86163*

**Room equipment**

Separate bath and

toilet

Separate washroom

Washroom with shower

**Building equipment**

Fiber optic Internet

Automatic entrance door

Garbage 24H available

**49**

**stock**

**Main stock**


Based on these features, there is found to be little difference between the physical space distribution due to the period of construction and only the building quality changes.

### **3.2 Distribution of analysis data by ward**

Next, we examined the distribution of old/main/new stock for each of the 23 wards (**Table 2**). The ratio of new stock ratio exceeds 70% in Chiyoda, Chuo, and Minato wards, which make up the center of Tokyo. As previously mentioned, the probability of large-scale redevelopment and so on being carried out increases for more urban areas, which may have caused this result due to active stock renewal.<sup>10</sup>

Outside the three wards of the city center, the ratio of new stock is over 70% in Taito and Sumida wards and over 60% in Koto and Shinagawa wards, but this may be due to the supply of large-scale high-rise condominiums due to the relaxation of regulations in the 1990s. The ratio of new stock in other wards is around 50% (**Table 3**).

### **3.3 Ancillary equipment rate by period of construction**

**Table 4** shows the ancillary equipment rate by period of construction. Equipment was classified into that ancillary to the room, ancillary to the building, and conditions of contract.<sup>11</sup>

Housing equipment items are arranged in descending order of ancillary rate in all samples, and a comparison is made between old, main, and new stocks.

In terms of room equipment, the five items (i) air conditioning, (ii) hot water supply, (iii) indoor washing machine area, (iv) separate bath and toilet, and (v) flooring have a high ancillary rate of over 80%. The equipments for which there is a large difference in ancillary rate between old and new stocks (ancillary rate increased) are bathroom dryer (+62.6%), system kitchen (+50.7%), toilet with washlet (+43.4%), indoor washing machine area (+38.0%), and separate washroom (+36.0%).

In terms of building equipment, the ancillary rate is over 50% for the bicycle parking lot and fiber optic Internet. The equipment for which there is a large difference in ancillary rate between old and new stocks (ancillary rate increased) is automatic entrance door (+65.0%), TV intercom (+55.4%), delivery locker

<sup>10</sup> This result is consistent with the results of Shimizu et al. [1].

<sup>11</sup> Although it seems that the housing equipment ancillary to the room and building fluctuates somewhat due to renewal and so on, the equipment seems to be influenced by the construction date. Attention must be given to the fact that contract conditions may be changed regardless of the construction date because physical investment is unnecessary.

**Number of observations Ratio Item Total Old stock Main stock New stock Total Old stock Main stock New stock New-old Room equipment** Air conditioning 49.088 4029 17.883 27.176 91.7% 82.8% 89.5% 94.7% 11.9% Hot water supply 44.841 3879 16.961 24.001 83.7% 79.7% 84.9% 83.6% 3.9% Indoor WM area 43.954 2663 14.696 26.595 82.1% 54.7% 73.5% 92.7% 38.0% Separate bath and toilet 43.943 3447 12.851 27.645 82.1% 70.8% 64.3% 96.3% 25.5% Flooring 43.269 3364 14.915 24.990 80.8% 69.1% 74.6% 87.1% 18.0% Balcony 40.851 3204 15.276 22.371 76.3% 65.8% 76.4% 77.9% 12.1% System kitchen 27.758 1093 5666 20.999 51.8% 22.5% 28.4% 73.2% 50.7% Separate washroom 26.292 1412 6221 18.659 49.1% 29.0% 31.1% 65.0% 36.0% 1 gas stove 25.300 1416 6396 17.488 47.2% 29.1% 32.0% 60.9% 31.8% Washlet 23.221 1089 3265 18.867 43.4% 22.4% 16.3% 65.7% 43.4% Bathroom dryer 20.322 186 1077 19.059 37.9% 3.8% 5.4% 66.4% 62.6% 2 gas stoves 18.632 1081 3304 14.247 34.8% 22.2% 16.5% 49.6% 27.4% Reheating bath 15.127 1268 3459 10.400 28.2% 26.0% 17.3% 36.2% 10.2% Washroom with shower 12.678 364 1764 10.550 23.7% 7.5% 8.8% 36.8% 29.3% Own house rental 7187 497 1588 5102 13.4% 10.2% 7.9% 17.8% 7.6% IH stovetop 6623 215 2653 3755 12.4% 4.4% 13.3% 13.1% 8.7% Walk-in closet 3694 88 235 3371 6.9% 1.8% 1.2% 11.7% 9.9% Counter kitchen 3409 70 516 2823 6.4% 1.4% 2.6% 9.8% 8.4% With loft 2110 19 754 1337 3.9% 0.4% 3.8% 4.7% 4.3% Underfloor heating 1147 8 87 1052 2.1% 0.2% 0.4% 3.7% 3.5% **Building equipment** Bicycle parking lot 33.795 2096 11.385 20.314 63.1% 43.1% 57.0% 70.8% 27.7% Fiber optic Internet 27.307 2085 10.056 15.166 51.0% 42.8% 50.3% 52.8% 10.0% TV intercom 26.689 953 4232 21.504 49.8% 19.6% 21.2% 74.9% 55.4% Automatic entrance door 26.042 337 5062 20.643 48.6% 6.9% 25.3% 71.9% 65.0% Cable TV 23.211 1316 8314 13.581 43.3% 27.0% 41.6% 47.3% 20.3% BS antenna 20.013 472 4430 15.111 37.4% 9.7% 22.2% 52.7% 43.0% Elevator 19.587 1189 5387 13.011 36.6% 24.4% 27.0% 45.3% 20.9% Tiling wall 15.751 561 5265 9925 29.4% 11.5% 26.3% 34.6% 23.1% Delivery locker 15.163 119 1550 13.494 28.3% 2.4% 7.8% 47.0% 44.6% Security camera 12.694 302 1849 10.543 23.7% 6.2% 9.3% 36.7% 30.5% CS antenna 11.888 304 1837 9747 22.2% 6.2% 9.2% 34.0% 27.7% Garbage 24H available 6670 130 728 5812 12.5% 2.7% 3.6% 20.3% 17.6% Bike parking lot 6335 354 1875 4106 11.8% 7.3% 9.4% 14.3% 7.0%

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing… DOI: http://dx.doi.org/10.5772/intechopen.86163*

• There is little difference between old and main stocks in average rent, but it is

• There is no significant difference in the average floor area, the number of minutes by foot from the nearest station, and the number of minutes by train

• Concerning the years since construction, the average of the total is 18.5 years and the standard deviation is 12.7 years, and the average value and standard

Based on these features, there is found to be little difference between the phys-

Next, we examined the distribution of old/main/new stock for each of the 23 wards (**Table 2**). The ratio of new stock ratio exceeds 70% in Chiyoda, Chuo, and Minato wards, which make up the center of Tokyo. As previously mentioned, the probability of large-scale redevelopment and so on being carried out increases for more urban areas, which may have caused this result due to active stock renewal.<sup>10</sup> Outside the three wards of the city center, the ratio of new stock is over 70% in Taito and Sumida wards and over 60% in Koto and Shinagawa wards, but this may be due to the supply of large-scale high-rise condominiums due to the relaxation of regulations in the 1990s. The ratio of new stock in other wards is around 50%

**Table 4** shows the ancillary equipment rate by period of construction. Equipment was classified into that ancillary to the room, ancillary to the building, and

Housing equipment items are arranged in descending order of ancillary rate in

In terms of room equipment, the five items (i) air conditioning, (ii) hot water

In terms of building equipment, the ancillary rate is over 50% for the bicycle parking lot and fiber optic Internet. The equipment for which there is a large difference in ancillary rate between old and new stocks (ancillary rate increased) is

<sup>11</sup> Although it seems that the housing equipment ancillary to the room and building fluctuates somewhat due to renewal and so on, the equipment seems to be influenced by the construction date. Attention must be given to the fact that contract conditions may be changed regardless of the construction date because

all samples, and a comparison is made between old, main, and new stocks.

supply, (iii) indoor washing machine area, (iv) separate bath and toilet, and (v) flooring have a high ancillary rate of over 80%. The equipments for which there is a large difference in ancillary rate between old and new stocks (ancillary rate increased) are bathroom dryer (+62.6%), system kitchen (+50.7%), toilet with washlet (+43.4%), indoor washing machine area (+38.0%), and separate washroom

automatic entrance door (+65.0%), TV intercom (+55.4%), delivery locker

<sup>10</sup> This result is consistent with the results of Shimizu et al. [1].

deviation by construction date are consistent with the classification.

ical space distribution due to the period of construction and only the building

about 20% higher for new stock than the old stock.

from Tokyo station.

*Modern Perspectives in Business Applications*

**3.2 Distribution of analysis data by ward**

**3.3 Ancillary equipment rate by period of construction**

quality changes.

(**Table 3**).

(+36.0%).

**48**

conditions of contract.<sup>11</sup>

physical investment is unnecessary.


### **Table 2.**

*Distribution of equipment in old stock, main stock, and new stock.*

(+44.6%), BS antenna (+43.0%), and security camera (+30.5%). In the conditions of contract, there are no items of note except for guarantor unnecessary, which is high at 37.8%, and only guarantor unnecessary (+19.6%) has a large difference in ancillary rate between old and new stocks (ancillary rate increased), but free Internet is also +11.8%.<sup>12</sup>

such as wooden buildings decreasing by 8.0% and SRC by 7.4%, while steel frames

**Ward Old stock Main stock New stock Total Old stock Main stock New stock** Chiyoda 39 65 342 446 8.7% 14.6% 76.7% Chuo 57 117 740 914 6.2% 12.8% 81.0% Minato 137 182 927 1246 11.0% 14.6% 74.4% Shinjuku 284 574 1264 2122 13.4% 27.0% 59.6% Bunkyo 144 379 706 1229 11.7% 30.8% 57.4% Taito 86 210 796 1092 7.9% 19.2% 72.9% Sumida 103 348 1077 1528 6.7% 22.8% 70.5% Kouto 134 454 1056 1644 8.2% 27.6% 64.2% Shinagawa 190 650 1463 2303 8.3% 28.2% 63.5% Meguro 134 537 789 1460 9.2% 36.8% 54.0% Ota 458 2022 3054 5534 8.3% 36.5% 55.2% Setagaya 494 2605 2450 5549 8.9% 46.9% 44.2% Shibuya 188 425 908 1521 12.4% 27.9% 59.7% Nakano 292 996 1367 2655 11.0% 37.5% 51.5% Suginami 421 1915 1778 4114 10.2% 46.5% 43.2% Toshima 189 687 1019 1895 10.0% 36.3% 53.8% Kita 300 797 1061 2158 13.9% 36.9% 49.2% Arakawa 100 339 582 1021 9.8% 33.2% 57.0% Itabashi 291 1254 1441 2986 9.7% 42.0% 48.3% Nerima 243 1639 1796 3678 6.6% 44.6% 48.8% Adachi 182 1020 1518 2720 6.7% 37.5% 55.8% Katsushika 177 926 1074 2177 8.1% 42.5% 49.3% Edogawa 225 1841 1492 3558 6.3% 51.7% 41.9% Total 4868 19,982 28,700 53,550 9.1% 37.3% 53.6%

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing…*

*DOI: http://dx.doi.org/10.5772/intechopen.86163*

**Table 5** shows the estimated results of the model. In addition, **Figure 1** illus-

<sup>13</sup> In addition, although the ratio of high-rise condominiums is rising, it is at about 1%, and even the

Looking at the estimated results, as floor area increases, rent goes up, and as the number of minutes on foot from the station increases or the railway travel time from Tokyo station increases, the rent goes down. When taking a wooden structure as the baseline of the building structure, the rent will increase in the order of steel frame, RC, and SRC. The rent varies greatly depending on the ward in which the property is located; a high-rise condominium is a positive driver, and a 1F

trates the dummy partial regression coefficients for the equipment.

increase by 5.6% and RC by 6.0%.<sup>13</sup>

number of 1F room positions is increasing.

*Spatial distribution of rental housing.*

**3.4 Estimated results**

**Table 3.**

**51**

Overall, the rise in security equipment is significant in building equipment, and the rise in the equipment that improves the living convenience is significant in room equipment. In addition, the ratio of building structures also shows changes,

<sup>12</sup> Traditionally, when renting out a house in the Japanese rental housing market, it is necessary to have a guarantor to hedge the risk of nonpayment of rent. Since the guarantor is liable in the case of unpaid rent, relatives often become the guarantor, but as the size of families decreases, it is becoming difficult to find a guarantor. Under such circumstances, rent-guarantee companies have appeared, and systems that eliminate the need for a guarantor by paying a set insurance premium have been introduced.


*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing… DOI: http://dx.doi.org/10.5772/intechopen.86163*

**Table 3.**

(+44.6%), BS antenna (+43.0%), and security camera (+30.5%). In the conditions of contract, there are no items of note except for guarantor unnecessary, which is high at 37.8%, and only guarantor unnecessary (+19.6%) has a large difference in ancillary rate between old and new stocks (ancillary rate increased), but free Inter-

*Distribution of equipment in old stock, main stock, and new stock.*

**Number of observations Ratio**

**Total Old stock**

6704 605 2702 3397 12.5% 12.4% 13.5% 11.8% -0.6%

1673 311 511 851 3.1% 6.4% 2.6% 3.0% -3.4%

387 3 54 330 0.7% 0.1% 0.3% 1.1% 1.1%

13.265 894 5217 7154 24.8% 18.4% 26.1% 24.9% 6.6%

**Main stock**

**New stock** **New-old**

**New stock**

Design by artist 4068 29 286 3753 7.6% 0.6% 1.4% 13.1% 12.5% Seismic structure 3827 37 702 3088 7.1% 0.8% 3.5% 10.8% 10.0%

with NO guarantor 20.257 1214 6274 12.769 37.8% 24.9% 31.4% 44.5% 19.6% No pets 8417 733 3540 4144 15.7% 15.1% 17.7% 14.4% -0.6%

NO office use 5253 297 1731 3225 9.8% 6.1% 8.7% 11.2% 5.1% FREE Internet 4682 100 616 3966 8.7% 2.1% 3.1% 13.8% 11.8% Pet consultation 3906 210 801 2895 7.3% 4.3% 4.0% 10.1% 5.8% Pets allowed 2189 150 437 1602 4.1% 3.1% 2.2% 5.6% 2.5%

Office use allowed 1319 362 508 449 2.5% 7.4% 2.5% 1.6% -5.9%

Wooden 10.851 1285 4273 5293 20.3% 26.4% 21.4% 18.4% -8.0% Steel frame 13.796 891 6044 6861 25.8% 18.3% 30.2% 23.9% 5.6% RC 23.654 2074 7635 13.945 44.2% 42.6% 38.2% 48.6% 6.0% SRC 3644 599 1626 1419 6.8% 12.3% 8.1% 4.9% -7.4% Others 1605 19 404 1182 3.0% 0.4% 2.0% 4.1% 3.7%

Overall, the rise in security equipment is significant in building equipment, and

<sup>12</sup> Traditionally, when renting out a house in the Japanese rental housing market, it is necessary to have a guarantor to hedge the risk of nonpayment of rent. Since the guarantor is liable in the case of unpaid rent, relatives often become the guarantor, but as the size of families decreases, it is becoming difficult to find a guarantor. Under such circumstances, rent-guarantee companies have appeared, and systems that

eliminate the need for a guarantor by paying a set insurance premium have been introduced.

the rise in the equipment that improves the living convenience is significant in room equipment. In addition, the ratio of building structures also shows changes,

net is also +11.8%.<sup>12</sup>

**Item Total Old**

*Modern Perspectives in Business Applications*

**Contract conditions**

NO musical instrument

Contract with limited term

**Others** High-rise block (16F over)

floor

**Table 2.**

**50**

Room on the first

**Building structure**

**stock**

**Main stock**

*Spatial distribution of rental housing.*

such as wooden buildings decreasing by 8.0% and SRC by 7.4%, while steel frames increase by 5.6% and RC by 6.0%.<sup>13</sup>

### **3.4 Estimated results**

**Table 5** shows the estimated results of the model. In addition, **Figure 1** illustrates the dummy partial regression coefficients for the equipment.

Looking at the estimated results, as floor area increases, rent goes up, and as the number of minutes on foot from the station increases or the railway travel time from Tokyo station increases, the rent goes down. When taking a wooden structure as the baseline of the building structure, the rent will increase in the order of steel frame, RC, and SRC. The rent varies greatly depending on the ward in which the property is located; a high-rise condominium is a positive driver, and a 1F

<sup>13</sup> In addition, although the ratio of high-rise condominiums is rising, it is at about 1%, and even the number of 1F room positions is increasing.


**Dependent**

**53**

 **variable** Taito Sumida

Koto Shinagawa

Meguro

Ota Setagaya

Shibuya Nakano Suginami

Toshima

Kita Arakawa

Itabashi Nerima

Adachi Katsushika

Edogawa Difference between max. and min.

**Table 4.** *Results of hedonic equations: main estimated results.*

**ln (monthly rent) JPY**

14.43%

15.60%

13.38%

6.09%

8.18%

11.59%

Baseline 10.74%

4.79%

5.20%

7.34%

16.82%

19.82%

15.50%

12.61%

27.31%

26.27%

21.84%

39.32%

 0.00

19.56%

41.79%

 0.00

21.90%

38.37%

 0.00

21.92%

39.10%

 0.00

2.35%

2.69%

 0.00

24.68%

 0.00

26.83%

 0.00

25.97%

 0.00

1.29%

 0.00

24.71%

 0.00

27.17%

 0.00

27.90%

 0.00

3.19%

 0.00

10.86%

 0.00

12.29%

 0.00

12.95%

 0.00

2.09%

 0.00

15.73%

 0.00

15.13%

 0.00

15.86%

 0.00

0.12%

 0.00

17.14%

 0.00

18.83%

 0.00

20.71%

 0.00

3.57%

 0.00

14.43%

 0.00

16.10%

 0.00

17.69%

 0.00

3.25%

 0.00

4.23%

 0.00

6.35%

 0.00

9.01%

 0.00

4.79%

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing…*

 0.00

4.42%

 0.00

4.99%

 0.00

5.64%

 0.00

1.22%

 0.00

3.33%

 0.00

 0.00

 12.23%

 0.00

 7.93% 3.76%

 0.00

6.01%

 0.00

2.68%

 0.00

 11.20%

 0.00

1.03%

 0.00

9.71%

Baseline

 0.00

10.11%

Baseline

 0.00

13.14%

Baseline

 0.00

3.42%

 0.00

 10.71%

 0.00

 6.95%

 0.00

 7.76%

 0.00

2.95%

*DOI: http://dx.doi.org/10.5772/intechopen.86163*

 0.00

2.83%

 0.02

6.83%

 0.00

6.62%

 0.00

3.79%

 0.00

12.53%

 0.00

12.56%

 0.00

13.80%

 0.00

1.27%

 0.00

15.02%

 0.00

14.04%

 0.00

16.21%

 0.00

1.19%

 0.00

12.95%

 0.00

14.68%

 0.00

14.73%

 0.00

1.78%

### *Modern Perspectives in Business Applications*


*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing… DOI: http://dx.doi.org/10.5772/intechopen.86163*

> **Table 4.**

*Results of hedonic equations: main estimated results.*

**Dependent**

**52**

Estimation method

Number of Adj R-squared

**Independent**

Age of unit (year) Old stock dummy Main stock dummy

New stock dummy

Floor space (m2

Time to Tokyo station (minutes)

Time to the nearest station (minutes)

Building

Structure

 Steel frame

RC SRC Others

> Wards

Chiyoda

Chuo

Minato Shinjuku

Bunkyo

 Wooden

)

 **variables**

**Mark**

**All**

**Coef.**

0.53%

1.53%

0.54%

Baseline

1.69%

0.70%

0.62%

Baseline

4.28%

9.26%

10.75%

4.01%

1.44%

3.77%

12.01%

0.54%

5.38%

 0.00

4.39%

 0.00

5.82%

 0.00

5.52%

 0.00

1.13%

 0.07

 3.46%

 0.00

 0.15%

 0.77

0.54%

 0.14

4.00%

 0.00

 17.08%

 0.00

 11.20%

 0.00

 10.75%

 0.00

6.34%

 0.00

 0.14%

 0.94

2.84%

 0.01

4.29%

 0.00

4.43%

 0.01

 8.72%

 0.00

 3.21%

 0.03

3.03%

 0.00

11.75%

 0.00

 6.83%

 0.11

 4.20%

 0.00

 3.25%

 0.00

3.58%

 0.00

 13.10%

 0.00

 9.72%

 0.00

 8.99%

 0.00

4.11%

 0.00

 13.04%

 0.00

 8.41%

 0.00

 8.17%

 0.00

4.87%

 0.00

 7.22%

 0.00

 3.67%

 0.00

 3.51%

 0.00

3.71%

 0.00

0.53%

Baseline

 0.00

0.61%

Baseline

 0.00

0.64%

Baseline

 0.00

0.11%

 0.00

0.67%

 0.00

0.77%

 0.00

 0.00

 1.60%

 0.00

 1.61%

 0.00

 1.73% 0.64%

 0.00

 0.03%

 0.00

 0.13%

 0.01

 (Omitted) (Omitted)

 0.00

 (Omitted)

 0.00

0.13%

 0.01

0.43%

(Omitted) (Omitted) (Omitted)

 0.00

 **P>t**

 **Coef.**

 **P>t**

 **Coef.**

 **P>t**

 **Coef.** 0.63%

(Omitted)

(Omitted)

(Omitted)

 0.00

0.50%

 **P>t**

Coef.

*Modern Perspectives in Business Applications*

**Old stock**

observations

 **variable**

**ln (monthly rent) JPY**

OLS

53,520

0.894

4867 0.853

19,975

0.897 **Main stock**

28,678

0.892

**New stock**

New-old


apartment positions a negative driver for rent. These results are consistent with

*Estimated results of room equipment (RE), building equipment (BE), and contract conditions (CC).*

\_cons 0.00% 0.00 0.00% 0.00 0.00% 0.00 0.00% 0.00 –

**Independent variables All Old stock Main stock New stock New-**

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing…*

Delivery locker

*DOI: http://dx.doi.org/10.5772/intechopen.86163*

Security camera

Garbage 24H available

Bike parking lot

Design by artist

Seismic structure

CC with NO guarantor

> Pet consultation

No musical instrument

Office use allowed

Contract with limited term

**Table 5.**

**55**

BS antenna 1.25% 0.00 2.58% 0.01 0.05% 0.83 1.45% 0.00 1.13% Elevator 2.52% 0.00 2.89% 0.00 2.10% 0.00 2.63% 0.00 0.26% Tiling wall 1.44% 0.00 1.91% 0.00 1.21% 0.00 0.97% 0.00 0.93%

CS antenna 0.60% 0.00 1.76% 0.15 0.69% 0.04 1.25% 0.00 0.51%

No pets 0.06% 0.77 1.46% 0.12 0.37% 0.25 0.47% 0.07 1.93%

Pets allowed 2.57% 0.00 2.17% 0.05 3.40% 0.00 2.35% 0.00 0.18%

No office use 0.81% 0.00 1.18% 0.18 0.01% 0.97 1.13% 0.00 2.31%

Free Internet **B** 0.82% 0.00 3.01% 0.02 0.58% 0.19 0.68% 0.00 2.33%

**Coef. P>t Coef. P>t Coef. P>t Coef. P>t Coef.**

**A** 2.03% 0.00 4.55% 0.00 1.42% 0.00 2.68% 0.00 1.87%

**C** 1.33% 0.00 0.62% 0.45 1.06% 0.00 1.61% 0.00 0.99%

**C** 0.13% 0.49 1.58% 0.18 0.84% 0.06 0.98% 0.00 2.56%

**C** 0.75% 0.00 0.38% 0.61 0.29% 0.28 0.94% 0.00 1.32%

0.45% 0.02 0.62% 0.80 1.78% 0.01 0.52% 0.01 0.09%

2.25% 0.00 4.11% 0.05 1.95% 0.00 1.82% 0.00 2.29%

3.24% 0.00 2.85% 0.00 4.13% 0.00 3.10% 0.00 0.25%

0.32% 0.15 1.61% 0.11 0.22% 0.54 0.83% 0.00 2.44%

**C** 5.04% 0.00 2.34% 0.00 4.01% 0.00 6.11% 0.00 3.77%

**B** 0.82% 0.00 2.80% 0.00 0.86% 0.08 0.30% 0.39 2.50%

**D** 0.82% 0.00 1.47% 0.00 1.07% 0.00 0.23% 0.08 1.24%

**old**

The effect of the number of years since construction differs depending on the period of construction, and as a whole, there is a 0.53% reduction in rent per year after construction. However, looking at the old/main/new period of construction dummy, the speed of reduction is high for new stock and low for old stock. This shows that the effect of years since construction is nonlinear, indicating that the decline in rent will be considerably smaller after a certain number of years. Such

previous studies and the intuition of market participants.

nonlinearity is also consistent with a series of previous studies.


*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing… DOI: http://dx.doi.org/10.5772/intechopen.86163*

**Table 5.**

**Independent variables All Old stock Main stock New stock New-**

Room on the first floor 2.76% 0.00 0.55% 0.28 2.94% 0.00 3.00% 0.00 2.44%

Flooring **A** 0.16% 0.22 3.35% 0.00 0.50% 0.01 1.81% 0.00 5.17%

balcony **A** 0.84% 0.00 3.28% 0.00 1.82% 0.00 0.01% 0.95 3.27%

1 gas stove 0.52% 0.00 1.09% 0.04 0.13% 0.52 1.13% 0.00 0.05% Washlet **A** 2.20% 0.00 3.17% 0.00 2.75% 0.00 1.16% 0.00 2.01%

2 gas stoves 0.34% 0.01 0.03% 0.95 1.17% 0.00 0.09% 0.55 0.05%

IH stovetop **D** 1.03% 0.00 0.65% 0.49 1.68% 0.00 1.62% 0.00 0.97%

With loft 4.72% 0.00 5.59% 0.07 4.08% 0.00 4.19% 0.00 1.40%

TV intercom **A** 1.08% 0.00 3.99% 0.00 1.71% 0.00 0.15% 0.34 4.14%

Cable TV 0.63% 0.00 1.61% 0.00 0.26% 0.13 0.51% 0.00 1.10%

High-rise block (16F

conditioning

*Modern Perspectives in Business Applications*

Hot water supply

Indoor WM area

Separate bath and toilet

System kitchen

Separate washroom

Bathroom dryer

Reheating bath

Washroom with shower

Own house rental

Walk-in closet

Counter kitchen

Underfloor heating

Fiber optic Internet

Automatic entrance door

BE Bicycle parking lot

**54**

over)

RE Air

**Coef. P>t Coef. P>t Coef. P>t Coef. P>t Coef.**

8.74% 0.00 14.12% 0.08 4.35% 0.01 9.22% 0.00 4.89%

0.82% 0.00 1.87% 0.00 0.18% 0.48 0.02% 0.96 1.86%

1.77% 0.00 0.58% 0.24 1.03% 0.00 2.42% 0.00 3.01%

1.27% 0.00 2.73% 0.00 1.73% 0.00 0.77% 0.00 3.50%

**A** 5.07% 0.00 5.58% 0.00 6.46% 0.00 1.55% 0.00 4.03%

1.85% 0.00 4.62% 0.00 2.40% 0.00 0.79% 0.00 3.83%

2.11% 0.00 2.18% 0.00 2.35% 0.00 2.16% 0.00 0.03%

**A** 1.35% 0.00 4.90% 0.00 3.07% 0.00 1.29% 0.00 3.61%

**C** 2.22% 0.00 0.26% 0.56 0.06% 0.82 3.45% 0.00 3.18%

1.16% 0.00 0.64% 0.41 0.18% 0.55 1.33% 0.00 1.97%

**D** 2.92% 0.00 0.54% 0.45 1.87% 0.00 3.42% 0.00 2.88%

**B** 1.22% 0.00 4.33% 0.00 0.77% 0.29 0.88% 0.00 3.45%

**C** 5.19% 0.00 1.55% 0.73 1.09% 0.36 4.97% 0.00 6.52%

0.94% 0.00 0.71% 0.09 0.70% 0.00 0.96% 0.00 0.25%

1.04% 0.00 1.83% 0.00 0.93% 0.00 0.91% 0.00 0.92%

**A** 1.74% 0.00 4.47% 0.00 2.72% 0.00 1.63% 0.00 2.84%

1.10% 0.00 2.83% 0.08 0.03% 0.95 0.72% 0.00 2.11%

**old**

*Estimated results of room equipment (RE), building equipment (BE), and contract conditions (CC).*

apartment positions a negative driver for rent. These results are consistent with previous studies and the intuition of market participants.

The effect of the number of years since construction differs depending on the period of construction, and as a whole, there is a 0.53% reduction in rent per year after construction. However, looking at the old/main/new period of construction dummy, the speed of reduction is high for new stock and low for old stock. This shows that the effect of years since construction is nonlinear, indicating that the decline in rent will be considerably smaller after a certain number of years. Such nonlinearity is also consistent with a series of previous studies.

In Pattern B, it is assumed that the price premium of the equipment was lost because the needs the equipment satisfied were limited in the first place and have been satisfied. The walk-in closet corresponds to this in room equipment (RE), nothing corresponds to this in building equipment (BE), and free Internet and contract with limited term correspond to this in contract conditions (CC). Contract

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing…*

• Pattern C: Items for which demand is considered to be increasing but the

Pattern C is such that although consumer demand is increasing over time, a price premium exists because of the low ancillary rate in the housing stock. Equipment such as a reheating bath and underfloor heating corresponds to this in room equipment (RE), and security cameras, garbage disposal available 24-hours a day, and bike parking correspond to this in building equipment (BE). Items such as use as an office correspond to this in contract conditions (CC). In particular, the reheating bath and use as an office have a significant influence of +3.45 and +3.77%,

• Pattern D: Items considered to be due to other individual factors

Items for which a price premium exists due to other factors correspond to ownerowned condominium for lease in room equipment (RE) and guarantor unnecessary in contract conditions (CC). Regarding condominium for lease, the effect of the increase in supply is considered to be caused by the change in the social situation, where the tendency for relatives to avoid guaranteeing rent obligations has

**3.5 Influence of ancillary equipment situation on equipment depreciation rate**

**Figure 2** shows the depreciation rate of all rents (All) and for the case where the ancillary equipment situation is poor (Poor). The equipment being poor indicates there is no (i) washlet, (ii) bathroom dryer, (iii) reheating bath, (iv) TV intercom, (iv) automatic entrance door, (iv) delivery locker, or (vii) security camera. These

with limited term has a negative impact on rent in new stock.

ancillary rate is low, and value is increasing

*DOI: http://dx.doi.org/10.5772/intechopen.86163*

respectively.

strengthened.

**Figure 2.**

**57**

*Depreciation in rental housing.*

### **Figure 1.** *Marginal price effect on RE, BE, and CC.*

The influence of the ancillary equipment situation on the rent changes according to the period of construction (**Figure 1**). The change can be classified into the following four patterns.<sup>14</sup>

• Pattern A: Items considered to have lost value because of commonness

In Pattern A, it is assumed that the equipment premium that was once a differentiating factor for price was lost because of the advancing commonness of equipment. This corresponds to room equipment (RE) such as flooring, separate bath and toilet, balcony, toilet with washlet, and bathroom dryer and building equipment (BE) such as TV intercom, automatic entrance door, delivery locker, and so on. In all cases, the ancillary rate has increased, so the superiority of the ancillary equipment falls, the influence on rent differs between old and new stocks, and such influence is generally small in new stock. Flooring and TV intercoms have a negative impact on new stock. This indicates that flooring and TV intercoms are no longer special equipment and do not offer price advantages.

• Pattern B: Items considered to have lost value because they satisfied limited needs

<sup>14</sup> Shimizu et al. [19] and Diewert and Shimizu [20–22] estimate a depreciation structure for the detached house and apartment market and the office market in Tokyo. The estimated results in this study show roughly the same form. As Diewert and Shimizu [23] covers the office market, durability is longer than for rental housing. Therefore, it has been reported that this will become a positive driver for rent at a stage exceeding 40 years after construction. The same tendency is observed in research targeting commercial real estate markets in Europe, the United States, and so on. The reason for this could be the influence of large costs for large-scale repairs and survivorship bias caused by higher-quality buildings having longer service life and only such buildings remaining. In this study, such bias is not observed, as it is limited to a certain period of time.

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing… DOI: http://dx.doi.org/10.5772/intechopen.86163*

In Pattern B, it is assumed that the price premium of the equipment was lost because the needs the equipment satisfied were limited in the first place and have been satisfied. The walk-in closet corresponds to this in room equipment (RE), nothing corresponds to this in building equipment (BE), and free Internet and contract with limited term correspond to this in contract conditions (CC). Contract with limited term has a negative impact on rent in new stock.

• Pattern C: Items for which demand is considered to be increasing but the ancillary rate is low, and value is increasing

Pattern C is such that although consumer demand is increasing over time, a price premium exists because of the low ancillary rate in the housing stock. Equipment such as a reheating bath and underfloor heating corresponds to this in room equipment (RE), and security cameras, garbage disposal available 24-hours a day, and bike parking correspond to this in building equipment (BE). Items such as use as an office correspond to this in contract conditions (CC). In particular, the reheating bath and use as an office have a significant influence of +3.45 and +3.77%, respectively.

• Pattern D: Items considered to be due to other individual factors

Items for which a price premium exists due to other factors correspond to ownerowned condominium for lease in room equipment (RE) and guarantor unnecessary in contract conditions (CC). Regarding condominium for lease, the effect of the increase in supply is considered to be caused by the change in the social situation, where the tendency for relatives to avoid guaranteeing rent obligations has strengthened.

### **3.5 Influence of ancillary equipment situation on equipment depreciation rate**

**Figure 2** shows the depreciation rate of all rents (All) and for the case where the ancillary equipment situation is poor (Poor). The equipment being poor indicates there is no (i) washlet, (ii) bathroom dryer, (iii) reheating bath, (iv) TV intercom, (iv) automatic entrance door, (iv) delivery locker, or (vii) security camera. These

**Figure 2.** *Depreciation in rental housing.*

The influence of the ancillary equipment situation on the rent changes according

to the period of construction (**Figure 1**). The change can be classified into the

• Pattern A: Items considered to have lost value because of commonness

differentiating factor for price was lost because of the advancing commonness of equipment. This corresponds to room equipment (RE) such as flooring, separate bath and toilet, balcony, toilet with washlet, and bathroom dryer and building equipment (BE) such as TV intercom, automatic entrance door, delivery locker, and so on. In all cases, the ancillary rate has increased, so the superiority of the ancillary equipment falls, the influence on rent differs between old and new stocks, and such influence is generally small in new stock. Flooring and TV intercoms have a negative impact on new stock. This indicates that flooring and TV intercoms are

• Pattern B: Items considered to have lost value because they satisfied limited

<sup>14</sup> Shimizu et al. [19] and Diewert and Shimizu [20–22] estimate a depreciation structure for the detached house and apartment market and the office market in Tokyo. The estimated results in this study show roughly the same form. As Diewert and Shimizu [23] covers the office market, durability is longer than for rental housing. Therefore, it has been reported that this will become a positive driver for rent at a stage exceeding 40 years after construction. The same tendency is observed in research targeting commercial real estate markets in Europe, the United States, and so on. The reason for this could be the influence of large costs for large-scale repairs and survivorship bias caused by higher-quality buildings having longer service life and only such buildings remaining. In this study, such bias is not

In Pattern A, it is assumed that the equipment premium that was once a

no longer special equipment and do not offer price advantages.

observed, as it is limited to a certain period of time.

following four patterns.<sup>14</sup>

*Marginal price effect on RE, BE, and CC.*

*Modern Perspectives in Business Applications*

**Figure 1.**

needs

**56**

types of equipment have become more common in recent years and can be installed in existing buildings.

• Some ancillary conditions have a large influence on rent, but if the ancillary rate increases, the influence becomes smaller due to commonness, and housing

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing…*

• Even if the number of years since construction is large, depreciation of the rent can be reduced if additional investment in appropriate housing equipment is

These evaluations are for the present time, and they are expected to change in the future as housing equipment ancillary rates change and social conditions, lifestyles, and resident demands evolve. The conclusion of this study shows the possibility of increasing profitability by responding to resident demands and raising rent through adding ancillary equipment, even in countries in Europe and in the United

However, several tasks remain. First, it is possible to add new functionality even to housing classified as old stock through large-scale renovation investment. In this sense, this study has not been able to measure the effect of investment in renovation. Moreover, in order to generalize the study result, it is necessary to identify appropriate housing equipment according to changes in lifestyle and social conditions, in addition to the influence of housing equipment on rent. Even if the scope is

The second author gratefully acknowledges the financial support of the Nomura

restricted to Japan, it is also necessary to consider points such as the type of differences that arise depending on the scale of the city and the standard of living and climate in different regions, as well as whether the necessary housing equipment differs according to the age, gender, family composition, income, and so on of

We would like to clarify these questions as future research tasks.

\*

\*Address all correspondence to: cshimizu@csis.u-tokyo.ac.jp

1 Institute of Future Design in Housing Market, Daito Trust Construction Co. Ltd.,

2 Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

States, where housing building restrictions are strict.

equipment responding to new needs have a positive influence on rent.

carried out.

*DOI: http://dx.doi.org/10.5772/intechopen.86163*

the residents.

Foundation.

**Author details**

Tokyo, Japan

**59**

Takeshi So1,2 and Chihiro Shimizu<sup>2</sup>

provided the original work is properly cited.

**Acknowledgements**

There were 13,033 properties with poor equipment; a regression analysis similar to the previous one was carried out with the logarithm of the rent as a target variable, and the regression coefficient of the years since construction was obtained. That is, as of October 2018, data points without the aforementioned equipment exist regardless of whether they are new, main, or old stock. This means that lowquality rental housing that does not have equipment that has become popular in recent years is still supplied. By extracting such data and comparing the depreciation of rental housing with new functions that benefited from technological progress and the depreciation of low-quality rental housing with no new functions, it is possible to extract the depreciation that accompanies obsolescence.

In **Figure 2**, the depreciation rate for each period is calculated with the rent at the time of construction as 100 to demonstrate the theoretical effect of the increasing number of years since construction on rent. When comparing the depreciation rate of all rents with that of rents of properties with a poor ancillary equipment situation, the depreciation rate increases in all cases (new, main, and old stocks). Roughly 60 years after construction, the difference was found to be 5.5%.

In addition to the measurement of the magnitude of the age effect accompanying obsolescence, this result means that rent depreciation can be mitigated if appropriate ancillary equipment investment is made with respect to the demands for housing equipment that have increased with economic growth and changes in lifestyle. We believe that this will provide pointers for high-level policy with respect to Japan's rental housing market, where the aging of stock will advance in the future.

### **4. Conclusion and future tasks**

Changes in prices over time are broken down into changes due to supply-demand relationships and those caused by quality changes. In particular, this means that in markets with rapid technological progress, the price rise accompanying quality change increases as new products are introduced successively, but at the same time, in markets where such new products are introduced, the speed of obsolescence is fast.

Compared with Western countries, new products are easy to create in the Japanese housing market. The background to this is there are many housing providers and a comparatively large number of companies that do business throughout Japan and overseas. Such companies possess, for example, think tanks to develop new products, and are developing integrated business from large-scale procurement of raw materials to design, construction, sales, and management.

In this study, we focused on the period in which the housing was supplied and clarified the types of functions and equipment supplied to the market in each period and the extent of the marginal price effect in 2018. In addition, we measured the magnitude of obsolescence that accompanies the addition of a new function.

The conclusion can be summarized as follows.


*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing… DOI: http://dx.doi.org/10.5772/intechopen.86163*


These evaluations are for the present time, and they are expected to change in the future as housing equipment ancillary rates change and social conditions, lifestyles, and resident demands evolve. The conclusion of this study shows the possibility of increasing profitability by responding to resident demands and raising rent through adding ancillary equipment, even in countries in Europe and in the United States, where housing building restrictions are strict.

However, several tasks remain. First, it is possible to add new functionality even to housing classified as old stock through large-scale renovation investment. In this sense, this study has not been able to measure the effect of investment in renovation. Moreover, in order to generalize the study result, it is necessary to identify appropriate housing equipment according to changes in lifestyle and social conditions, in addition to the influence of housing equipment on rent. Even if the scope is restricted to Japan, it is also necessary to consider points such as the type of differences that arise depending on the scale of the city and the standard of living and climate in different regions, as well as whether the necessary housing equipment differs according to the age, gender, family composition, income, and so on of the residents.

We would like to clarify these questions as future research tasks.

### **Acknowledgements**

types of equipment have become more common in recent years and can be installed

to the previous one was carried out with the logarithm of the rent as a target variable, and the regression coefficient of the years since construction was obtained. That is, as of October 2018, data points without the aforementioned equipment exist regardless of whether they are new, main, or old stock. This means that lowquality rental housing that does not have equipment that has become popular in recent years is still supplied. By extracting such data and comparing the depreciation of rental housing with new functions that benefited from technological progress and the depreciation of low-quality rental housing with no new functions, it is

possible to extract the depreciation that accompanies obsolescence.

of raw materials to design, construction, sales, and management.

improvement of living standards by economic growth.

The conclusion can be summarized as follows.

provided by previous studies.

**58**

There were 13,033 properties with poor equipment; a regression analysis similar

In **Figure 2**, the depreciation rate for each period is calculated with the rent at the time of construction as 100 to demonstrate the theoretical effect of the increasing number of years since construction on rent. When comparing the depreciation rate of all rents with that of rents of properties with a poor ancillary equipment situation, the depreciation rate increases in all cases (new, main, and old stocks). Roughly 60 years after construction, the difference was found to be 5.5%.

In addition to the measurement of the magnitude of the age effect accompanying obsolescence, this result means that rent depreciation can be mitigated if appropriate ancillary equipment investment is made with respect to the demands for housing equipment that have increased with economic growth and changes in lifestyle. We believe that this will provide pointers for high-level policy with respect to Japan's rental housing market, where the aging of stock will advance in the future.

Changes in prices over time are broken down into changes due to supply-demand relationships and those caused by quality changes. In particular, this means that in markets with rapid technological progress, the price rise accompanying quality change increases as new products are introduced successively, but at the same time, in markets where such new products are introduced, the speed of obsolescence is fast. Compared with Western countries, new products are easy to create in the Japanese housing market. The background to this is there are many housing providers and a comparatively large number of companies that do business throughout Japan and overseas. Such companies possess, for example, think tanks to develop new products, and are developing integrated business from large-scale procurement

In this study, we focused on the period in which the housing was supplied and clarified the types of functions and equipment supplied to the market in each period and the extent of the marginal price effect in 2018. In addition, we measured the magnitude of obsolescence that accompanies the addition of a new function.

• Rent is strongly influenced by the floor area, years since construction, building structure, number of minutes on foot from the nearest station, railway travel time from Tokyo station, location, and so on. This confirms conclusions

• The ancillary conditions of housing equipment vary greatly depending on the construction year. This suggests that the Japanese rental housing market is strongly influenced by regulations such as the *Building Standards Act* and the

in existing buildings.

*Modern Perspectives in Business Applications*

**4. Conclusion and future tasks**

The second author gratefully acknowledges the financial support of the Nomura Foundation.

### **Author details**

Takeshi So1,2 and Chihiro Shimizu<sup>2</sup> \*

1 Institute of Future Design in Housing Market, Daito Trust Construction Co. Ltd., Tokyo, Japan

2 Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan

\*Address all correspondence to: cshimizu@csis.u-tokyo.ac.jp

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Shimizu C, Karato K, Asami Y. Estimation of redevelopment probability using panel data-asset bubble burst and office market in Tokyo. Journal of Property Investment and Finance. 2010;**28**(4):285-300

[2] Sirmans GS, David A, Emily N. The composition of hedonic pricing models. Journal of Real Estate Literature. 2005; **13**(1):1-44

[3] Yoo S, Im J, Wagner JE. Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY. Landscape and Urban Planning. 2012; **107**(3):293-306

[4] Lancaster K. A new approach to consumer theory. Journal of Political Economy. 1966;**74**(2):132-157

[5] Rosen S. Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy. 1974;**82**(1):34-55

[6] Shimizu C, Karato K. Property price index theory and estimation: A survey. CSIS Discussion Paper Series. The University of Tokyo; 2018. Available from: http://www.csis.u-tokyo.ac.jp/ wp-content/uploads/2018/11/156.pdf

[7] Ekeland I, Heckman J, Nesheim L. Identification and estimation of hedonic models. Journal of Political Economy. 2004;**112**(S1):S60-S109

[8] Nishi H, Asami Y, Shimizu C. Housing features and rent: Estimating the microstructures of rental housing. International Journal of Housing Market and Analysis. 2018. forthcoming. DOI: 10.1108/IJHMA-09-2018-0067

[9] Billings SB. Hedonic amenity valuation and housing renovations. Real Estate Economics. 2015;**43**(3):652-682

[10] McMillen DP, Thorsnes P. Housing renovations and the quantile repeat sales price index. Real Estate Economics. 2006;**34**(4):567-584

of house prices. Asian Economic Journal. 2015;**29**(4):325-345

459-488

1659-1714

Columbia; 2017

**61**

[19] Shimizu C, Nishimura KG, Karato K. Nonlinearity of housing price structure—Secondhand condominium market in Tokyo Metropolitan Area. International Journal of Housing Markets and Analysis. 2014;**7**(3):

*DOI: http://dx.doi.org/10.5772/intechopen.86163*

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing…*

[20] Diewert WE, Shimizu C. Residential property price indexes for Tokyo. Macroeconomic Dynamics. 2015;**19**(8):

[21] Diewert WE, Shimizu C. Hedonic

condominium sales. Regional Science and Urban Economics. 2016;**60**:300-315

[22] Diewert WE, Shimizu C. Alternative approaches to commercial property price indexes for Tokyo. Review of Income and Wealth. 2017;**63**(3):492-519

[23] Diewert WE, Shimizu C. Alternative land price indexes for commercial properties in Tokyo. In: Discussion Paper 17–07. Vancouver School of Economics: University of British

regression models for Tokyo

[11] Shimizu C. Estimation of hedonic single-family house price function considering neighborhood effect variables. Sustainability (Switzerland). 2014;**6**(5):2946-2960

[12] Gao X, Asami Y. The external effects of local attributes on living environment in detached residential blocks in Tokyo. Urban Studies. 2001; **38**(3):487-505

[13] Fuerst F, Shimizu C. The rise of ecolabels in the Japanese housing market. Journal of Japanese and International Economy. 2016;**39**:108-122

[14] Nelson RH. Housing facilities, site advantages and rent. Journal of Regional Science. 1972;**12**(2):249-259

[15] Chau KW, Chin TL. A critical review of literature on the hedonic price model. International Journal for Housing Science and Its Applications. 2003;**2**(27):145-165

[16] Shimizu C, Nishimura K, Watanabe T. Housing prices in Tokyo: A comparison of hedonic and repeat sales measures. Journal of Economics and Statistics. 2010b;**230**:792-813

[17] Shimizu C, Takatsuji H, Ono H, Nishimura KG. Structural and temporal changes in the housing market and hedonic housing price indices. International Journal of Housing Markets and Analysis. 2010c;**3**(4): 351-368

[18] Karato K, Movshuk O, Shimizu C. Semiparametric estimation of time, age and cohort effects in an hedonic model

*Supply Management of Rental Housing Facilities: Effect of Changes in the Quality of Housing… DOI: http://dx.doi.org/10.5772/intechopen.86163*

of house prices. Asian Economic Journal. 2015;**29**(4):325-345

**References**

**13**(1):1-44

**107**(3):293-306

[1] Shimizu C, Karato K, Asami Y. Estimation of redevelopment probability using panel data-asset bubble burst and office market in Tokyo. Journal of Property Investment and Finance. 2010;**28**(4):285-300

*Modern Perspectives in Business Applications*

[10] McMillen DP, Thorsnes P. Housing renovations and the quantile repeat sales price index. Real Estate Economics.

[11] Shimizu C. Estimation of hedonic single-family house price function considering neighborhood effect variables. Sustainability (Switzerland).

[12] Gao X, Asami Y. The external effects of local attributes on living environment in detached residential blocks in Tokyo. Urban Studies. 2001;

[13] Fuerst F, Shimizu C. The rise of ecolabels in the Japanese housing market. Journal of Japanese and International

[14] Nelson RH. Housing facilities, site advantages and rent. Journal of Regional

Economy. 2016;**39**:108-122

Science. 1972;**12**(2):249-259

[15] Chau KW, Chin TL. A critical review of literature on the hedonic price

model. International Journal for Housing Science and Its Applications.

T. Housing prices in Tokyo: A

[16] Shimizu C, Nishimura K, Watanabe

comparison of hedonic and repeat sales measures. Journal of Economics and Statistics. 2010b;**230**:792-813

[17] Shimizu C, Takatsuji H, Ono H, Nishimura KG. Structural and temporal changes in the housing market and hedonic housing price indices. International Journal of Housing Markets and Analysis. 2010c;**3**(4):

[18] Karato K, Movshuk O, Shimizu C. Semiparametric estimation of time, age and cohort effects in an hedonic model

2003;**2**(27):145-165

351-368

2006;**34**(4):567-584

2014;**6**(5):2946-2960

**38**(3):487-505

[2] Sirmans GS, David A, Emily N. The composition of hedonic pricing models. Journal of Real Estate Literature. 2005;

[3] Yoo S, Im J, Wagner JE. Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY. Landscape and Urban Planning. 2012;

[4] Lancaster K. A new approach to consumer theory. Journal of Political Economy. 1966;**74**(2):132-157

competition. Journal of Political Economy. 1974;**82**(1):34-55

[5] Rosen S. Hedonic prices and implicit markets: Product differentiation in pure

[6] Shimizu C, Karato K. Property price index theory and estimation: A survey. CSIS Discussion Paper Series. The University of Tokyo; 2018. Available from: http://www.csis.u-tokyo.ac.jp/ wp-content/uploads/2018/11/156.pdf

[7] Ekeland I, Heckman J, Nesheim L. Identification and estimation of hedonic models. Journal of Political Economy.

[8] Nishi H, Asami Y, Shimizu C. Housing features and rent: Estimating the microstructures of rental housing. International Journal of Housing Market and Analysis. 2018. forthcoming. DOI:

10.1108/IJHMA-09-2018-0067

[9] Billings SB. Hedonic amenity

**60**

valuation and housing renovations. Real Estate Economics. 2015;**43**(3):652-682

2004;**112**(S1):S60-S109

[19] Shimizu C, Nishimura KG, Karato K. Nonlinearity of housing price structure—Secondhand condominium market in Tokyo Metropolitan Area. International Journal of Housing Markets and Analysis. 2014;**7**(3): 459-488

[20] Diewert WE, Shimizu C. Residential property price indexes for Tokyo. Macroeconomic Dynamics. 2015;**19**(8): 1659-1714

[21] Diewert WE, Shimizu C. Hedonic regression models for Tokyo condominium sales. Regional Science and Urban Economics. 2016;**60**:300-315

[22] Diewert WE, Shimizu C. Alternative approaches to commercial property price indexes for Tokyo. Review of Income and Wealth. 2017;**63**(3):492-519

[23] Diewert WE, Shimizu C. Alternative land price indexes for commercial properties in Tokyo. In: Discussion Paper 17–07. Vancouver School of Economics: University of British Columbia; 2017

**63**

Section 3

Sales Management in the

New Age

## Section 3
