price increase ratio comparison by type of housing

Price Increase Ratio Comparison byType of Housing : Focused on

Apartments, Single-Family Houses,Tenement houses

Jeong-Il, Choi1 and Ok-Dong, Lee2

1Division of Business,Sungkyul University,

Anyang, Gyeonggi-do, [email protected]

2Division of Tourism Development,Sungkyul University,

Anyang, Gyeonggi-do, [email protected]

Abstract

Background/Objectives: The purpose of this studyis to compare the rate of price increase for each type ofapartment, single-family house and tenement house.

Methods/Statistical Analysis: For this purpose, weexamined the data of apartment, single-family house andtenement houses from January 2000 through the index anal-ysis, numerical analysis and model analysis. As of the endof 2016, in the housing market it accounted apartmentsfor 48.1%, single-family houses 35.3% and tenement houses2.2%.

Findings: From January 2000(=100) to October 2017,the rate of increase is 245.5% for apartments, 173.1% fortown houses and 134.4% for single-family houses. The in-vestment value was in the order of apartments, tenementhouses, and single-family houses. In the correlation analysis,

International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 5687-5701ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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the correlation coefficients are 0.773 for apartments and sin-gle houses, and 0.760 for apartments and tenement houses.In the regression Analysis, housing purchase price compos-ite indices seem to have influenced the order of apartments,single-family houses and tenement houses. All of the apart-ments, single-family houses and tenement houses were sta-tistically significant at t-statistic and p-value(≤0.001). Inscatter charts analysis, compared with HPPCI, APT, HOUand TENE have a high degree of dispersion overall, show-ing high dynamic performance. In Quantile-Quantile Plotanalysis, there are a lot of point(◦) showing temporary fluc-tuation at the top and bottom of apartment, single-familyhouse and tenement houses. The housing market has changeda lot over the past 214 months.

Improvements/Applications: There is a very highcorrelation between single-family houses and tenement housesat 0.865. Apartments, single-family houses and tenementhouses showed a very high degree of co-existence with aslight difference.

Key Words : By type of Housing, Price Increase Rate,Apartment, Single-family House, Tenement house

1 Introduction

A single-family house has no limit on the number of floors andareas, and is a general house registered in the name of a singleperson. It is a house built on a single residential area for a singlehousehold, and it is a house that meets personal taste. A tenementhouse is a house with a total floor area of 660m2 or more and4 floors or less. An apartment can be understood similar to atenement house, but it is simply called an apartment if it is morethan five floors. Apartments are classified into hall type, stair typeand duplex type according to the layout method of the unit plane[1].Looking at Kookmin Bank’s housing price trend as of September2017, the median price was 295.48 million won for housing, 161.64million won for a tenement house, 333.2 million won for a single-family house and 316.45 million won for an apartment. Tradingcliffs appear after the 8.2 Real Estate Measures, and the declinein the transactions of the tenement houses and the single-family

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houses is larger than the apartments nationwide. The purpose ofthis study is to compare the rate of price increase for each typeof apartment, single-family house and tenement house. For thispurpose, I will examine the data of apartment, single-family houseand tenement houses from January 2000 through the index analysis,numerical analysis and model analysis. We expect to see a similarrate of rise in apartments, single-family homes and tenement housesto stabilize the housing market.

2 Precedent Research

In residential satisfaction of detached house and apartment resi-dents, Shim etc. 2 (2014) were highly perceived that in the caseof a single-family house, the value of investment, park green space,neighboring life, consciousness level, and in the case of apartment,neighboring life, consciousness level, neighborhood security[2]. Rhooand Seo (2016) studied the characteristics of rental market by typeof housing in Seoul[3], and Park (2013) identified the factors thataffect the price of urban living house[4].

Lee (2015) selected the following factors as factors influencingthe trading price of the housing market in the metropolitan area: the elderly population ratio, the proportion of one person, thedivorce rate for marriage, the number of businesses, the proportionof welfare budget, the local tax, the area of the park, the number ofelementary school students, housing depreciation, building permis-sion area, land price change rate[5]. Jung (2015) classified housingtypes into single-family houses, tenement houses, apartments, andanalyzed the volume time series data to determine the correlation.As a analysis result, it was shown that the tenement house had aweak negative(-) relationship in Seoul compared to the single-familyhouse and apartment, and showed a weak positive(+) relationshipin the metropolitan area[6].

Ko (2014) analyzed the change rate in the price of apartmentsin Seoul, which is the change in the official land price, station area,old-fashioned apartment sales, apartment transaction volume, pres-ence/absence of development, household growth rate[7]. Kim andKim (2014) confirmed that there is a long-term balanced relation-ship and a causal relationship in the correlation analysis of apart-


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ment price types[8].Kim and Lee (2013) suggested that the following items are

needed to improve the urban environment. First, interlayer noiseand ventilation in the case of housing, second, parks and squares toimprove the quality of life, third, subway stations, bus stops, sportsfacilities for residents[9]. Hong (2013) was selected as a factor toenhance the residential satisfaction of the urban living house, theconvenience facilities, the public welfare, the welfare and the con-venience facilities and the low rent[10]. Yoo (2007) analyzed thatthe selling price of apartments, single-family houses and tenementhouses is affected by lease price and building permission area. Healso suggested that interest rate cuts would increase demand forhousing and thus house prices[11].

Kim etc. 2 (2010) analyzed that the presence of unsold pre-salehousing affects the decline in the selling price. In addition, it sug-gests that unsold housing will increase in quantity when housingprices fall[12]. Oh etc. 2 (2009) analyzed the expectation psychol-ogy of Korean housing price trend as follows. Housing prices areclosely related to the real economy and have a more prominent im-pact on Korea. Statistical analysis related to housing finance isuseful for identifying trends in housing prices and establishing realestate measures by the government[13].

3 Data Collection and Indicator

Analysis

3.1 Data Collection

This study is divided the nationwide houses into apartments, single-family houses and tenement houses to compare the price increaserate by housing type. The analysis period was total 214 monthsfrom January 2000 to October 2017. Apartment, single-familyhouses and tenement houses data were collected from KookminBank Real Estate Statistics and Statistical Office National Sta-tistical Portal. For the sake of convenience, HPPCI for the na-tionwide houses, APT for apartment, HOU for single-family houseand TENE for tenement house. For each monthly data, we usedSPSS, Excel, and e-views to analyze indicators such as growth rate


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and change rate, descriptive statistics, correlation analysis and re-gression analysis. For model analysis, normal distribution, scatter,quantile-quantile and box-plot analysis were used. Through eachanalysis, we want to understand past real estate trends and futuredirections[14].

3.2 Indicator Analysis

Figure 1: Trends by Housing Type

Since 2006, the proportion of apartments, single-family housesand tenement houses has been shown in [Figure 1]. In the ’70s and’80s, the proportion of single-family houses accounted for more than90%, but the proportion gradually decreased since the ’90s. Sincethe beginning of the 1990s, the construction of Five New Towns hasbeen followed by a massive expansion of housing supply (2 millionsupplies). As of the end of 2016, in the housing market it accountedapartments for 48.1%, single-family houses 35.3% and tenementhouses 2.2%. As the supply of high floors apartments became morecommon, the proportion of single-family houses declined to lessthan 40% and prices fell.

Figure 2: MoM Change Rate


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[Figure 2] shows the rate of increase in the prices of apartments,single-family houses and tenement houses across the country. Asof January 2000(=100), the rate of increase is high in the order ofapartments, tenement houses and single-family houses. The rateof increase is 245.5% for apartments, 173.1% for town houses and134.4% for single-family houses. Since the early 2000s, the rateof increase in apartments has risen sharply compared to that ofsingle-family houses and tenement houses. The investment valuewas in the order of apartments, tenement houses, and single-familyhouses.

Figure 3: Trend of Monthly Change Rate

The monthly change rate of prices in the nationwide houses,apartments, single-family houses and tenement houses in the coun-try is shown in [Figure 3]. As of January 2000(=0), the rate ofchange has been strong in the order of apartments, tenement housesand single houses. Although the real estate market showed a strongupturn from 2000 to 2008, the rate of change has been relativelyweak since 2008. Since 2012, the volume of transactions has de-creased and the rate of change in apartments, tenement houses,and single-family homes has decreased. The change rate is con-verging and shows a direction soon, and predicts the possibility ofchange of the up or down[15].


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4 Empirical Analysis

4.1 Numerical Analysis

The descriptive statistics of apartments, single-family housesand tenement houses are shown in <Table 1>. In the case of theaverage, 0.42% of apartments, 0.26% of tenement houses and 0.14%of single houses are shown in order. The standard deviation is 0.72for apartments, 0.55 for tenement homes and 0.29 for single-familyhouses. In order of apartment, tenement house, single house, theaverage and standard deviation are high, indicating ’high profitand high risk’. Skewness is positive(+) in the order of apartments,tenement houses and single houses. As a result, it is judged thatthe apartment, tenement house and single house are closely packedin the upper part in order. Kurtosis has a positive(+) order inthe order of apartment, tenement house and single house. As aresult, it is judged that densely populated areas are formed aroundthe average in the order of apartments, tenement houses and singlehouses.


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The relationship between apartment, single-family houses andtenement houses is shown in <Table 2>. The correlation coeffi-cients are 0.773 for apartments and single houses, and 0.760 forapartments and tenement houses. There is a very high correla-tion between single-family houses and tenement houses at 0.865.Apartments, single-family houses and tenement houses showed avery high degree of co-existence with a slight difference.

Regression analysis of apartments, single-family house and ten-ement houses is shown in <Table 3>. Housing purchase price com-posite indices(HPPCI) are dependent variables, and apartments,single-family homes and tenement houses are independent vari-ables. Coefficient is 0.5656 for apartments, 0.2910 for single-familyhomes and 0.1444 for tenement houses. Housing purchase pricecomposite indices seem to have influenced the order of apartments,single-family houses and tenement houses. All of the apartments,single-family houses and tenement houses were statistically signif-icant at t-statistic and p-value(≤0.001). The adjusted R-squaredwas 0.9968, and the change in housing purchase price compositeindices in the regression model showed a high explanatory power of99.68%. Durbin-Watson stat value is 1.4632, which means that theindicators are moving independently of each other.


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4.2 Model Analysis

Figure 4: Histogram and stats

The monthly Histogram and stats of apartments, single-familyhouses and tenement houses represented in [Figure 4]. Histogramis a method of plotting data of frequency distribution[17]. Thehistogram is a graph of the frequency distribution table. Generallythe horizontal axis indicates the rank and the vertical axis indicatesthe frequency. For apartments, the mean is 0.42%, median 0.22%,S.D 0.72. For single-family houses, the mean is 0.13%, median0.11%, S.D 0.28. For tenement houses, the mean is 0.25%, median0.11%, S.D 0.55.

Figure 5: Scatter Plot

Monthly Scatter Charts for apartments, single-family housesand tenement houses represented in [Figure 5]. The X axis is theHPPCI variation rate, and the Y axis shows the variation rate of


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APT, HOU and TENE. Compared with HPPCI, APT, HOU andTENE have a high degree of dispersion overall, showing high dy-namic performance.

Figure 6: Quantile-Quantile Plot

The monthly Quantile-Quantile Plot of the nationwide houses,apartments, single-family houses and tenement houses representedin [Figure 6]. The figure shows the output data of each value cen-tered on the 1:1 line(red line). Single-family houses are movingcloser to 1:1 line relative to apartments and tenement houses. Inthe picture, there are a lot of point(◦) showing temporary fluctua-tion at the top and bottom of apartment, single-family house andtenement houses. The housing market has changed a lot over thepast 214 months.

Figure 7: Box-Box Plot

Monthly Box-Box Plots of the nationwide houses, apartments,single-family houses and tenement houses is shown in [Figure 7]. Inthe figure, the median value is higher than average of the nationwidehouses, apartments, single-family houses and tenement houses. The


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rate of change is shown in the order of apartments, tenement housesand single houses. There are a lot of abnormal surges (*) in theapartments and the tenement houses in the upper part. In the caseof single-family houses, there are many abnormal surges (*) at thetop and bottom.

5 Conclusion

The purpose of this study is to compare the rate of price increase foreach type of apartment, single-family house and tenement house.For this purpose, we examined the data of apartment, single-familyhouse and tenement houses from January 2000 through the indexanalysis, numerical analysis and model analysis. In the ’70s and’80s, the proportion of single-family houses accounted for more than90%, but the proportion gradually decreased since the ’90s. As ofthe end of 2016, in the housing market it accounted apartmentsfor 48.1%, single-family houses 35.3% and tenement houses 2.2%.As the supply of high floors apartments became more common, theproportion of single-family houses declined to less than 40% andprices fell.

From January 2000(= 100) to October 2017, the rate of in-crease is high in the order of apartments, tenement houses andsingle-family houses. The rate of increase is 245.5% for apartments,173.1% for town houses and 134.4% for single-family houses. Theinvestment value was in the order of apartments, tenement houses,and single-family houses. As of January 2000(=0), the rate ofchange has been strong in the order of apartments, tenement housesand single houses. In the correlation analysis, the correlation co-efficients are 0.773 for apartments and single houses, and 0.760 forapartments and tenement houses. There is a very high correla-tion between single-family houses and tenement houses at 0.865.Apartments, single-family houses and tenement houses showed avery high degree of co-existence with a slight difference.

In the regression Analysis, housing purchase price compositeindices(HPPCI) are dependent variables, and apartments, single-family homes and tenement houses are independent variables. Co-efficient is 0.5656 for apartments, 0.2910 for single-family homesand 0.1444 for tenement houses. Housing purchase price composite


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indices seem to have influenced the order of apartments, single-family houses and tenement houses. All of the apartments, single-family houses and tenement houses were statistically significant att-statistic and p-value(≤0.001).

In the monthly Histogram and stats. for apartments, the meanis 0.42%, median 0.22%, S.D 0.72. For single-family houses, themean is 0.13%, median 0.11%, S.D 0.28. For tenement houses, themean is 0.25%, median 0.11%, S.D 0.55. In scatter charts anal-ysis, compared with HPPCI, APT, HOU and TENE have a highdegree of dispersion overall, showing high dynamic performance. InQuantile-Quantile Plot analysis, there are a lot of point(◦) show-ing temporary fluctuation at the top and bottom of apartment,single-family house and tenement houses. The housing market haschanged a lot over the past 214 months. In Box-Box Plots analysis,the rate of change is shown in the order of apartments, tenementhouses and single houses. There are a lot of abnormal surges(*)in the apartments and the tenement houses in the upper part. Inthe case of single-family houses, there are many abnormal surges(*)and plunge(*) at the top and bottom.

References

[1] Lee, J. A. (2012). Analysis of Housing Behavior and MarketTrends of Single and Multi-House, Management Institute CEOREPORT 2012-02, 1-22.

[2] Shim, J. S. etc. 2 (2014). A Comparative Study on the Hous-ing Satisfactions and Influence factors Between Residents ofApartment and Single Housing, Geographical journal of Ko-rea, 48(1), 1-16.

[3] Rhoo, G. M. & Seo, M. G. (2016). Housing Rental Market byTypes of House in Korea, Urban research & insight, 6(1), 1-15.

[4] Park, H. M. (2013). An Analysis of the Factors Affecting onthe Prices of the Urban-life Housing Type of the Structureof the Urban Spaces in Seoul, Journal of the Korea plannersassociation, 48(3), 104-118.-


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[5] Lee, Y. H. (2015). An Analysis on Influential Factors TowardsNational Housing Market Segmentation and Segmental Hous-ing Price Change Due to Volatility of the Housing Price inSouth Korea, Korea Economic Review, 33(4), 31-60.

[6] Jung, D. S. (2015). An Analysis of the Dynamic Correlationbetween the Trading Volume by Housing Type, GRI ResearchBulletin, 17(3), 113-137.

[7] Go, J. W. (2014). An Analysis of Determinants of HousingPrice Change by Types of Apartment Purchase Market inSeoul, Korea Real Estate Academy Review, 58, 116-127.

[8] Kim, G. H. & Kim, J. T. (2014). A Relationship analysis ofthe leading or lagging through the Correlation analysis amongapartment price types, Journal of Korean Housing and Envi-ronment Society, 12(2), 183-197.

[9] Kim, S. B. & Lee, J. H. (2013). Analysis of Preference Factorfor Residential Environment in Types of Urban-Life-Housing,Korea Real Estate Academy Review, 53, 45-56.

[10] Hong, H. G. (2013). Determinants of Residential Satisfactionand Attachment in the Multi-Family Dwelling of Studio-Type,Journal of the Korean Housing Association, 24(3), 223-231.

[11] Yoo, J. S. (2007). A Study of determination of the housingprices and housing occupancy choice, Applied Economy, 9(1),199-217.

[12] Kim, S. H., Lee, S. H. & Kim, J. J. (2010). The Influence ofthe Housing Transaction and Jeonse Rental Price Upon theFluctuation of the Unsold Housing Stocks, Journal of the Ar-chitectural Institute of Korea, 26(1), 279-286.

[13] Oh, M. S., Oh, H. S. & Kwon, K. S. (2009). Analysis of HousePrice Expectancy in Korea, Korea Industrial Economics Jour-nal, 1(1), 101-117.

[14] Choi, J. I. & Lee, O. D. (2013). Study on the factors that affectthe fluctuations in the price of real estate for a digital economy,Journal of Digital Convergence, 11(11), 59-70.


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[15] Choi, J. I. & Lee, O. D. (2017). Analysis of Correlation andGrowth Rate between Housing Sales Price and Charter Priceof Seoul and 5 Metropolitan Cities, Korea Real Estate AcademyReview, 68, 97-106.

[16] Kookmin bank : www.kbstar.com

[17] Statistical Office : www.kostat.go.kr


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price increase ratio comparison by type of housing

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