# Development of Simulation Model for Proper Sales Price of Apartment House in Seoul

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Discussion

#### 3.1. Deriving the Relation between Pre-Sale Prices and Pre-Sale Rate

#### 3.2. Deriving the General Apartment Demand Curve

^{2}, 50% if the price is 1985 USD per 1 m

^{2}, and 100% if the price is 1758 USD per 1 m

^{2}. The current study focused on the range where the UTP demand curve fluctuated. As a result of analyzing the graph, the demand curve showed a radical decrease in demand probability at 1858 USD per 1 m

^{2}and only a moderate decrease from 2182 USD. The current study defines the range of radical fluctuation as the variable section.

^{2}and the probability of demand derived was 95.21% when analyzed by linear regression. Also, the demand probability was 0% at 2166 USD/m

^{2}, so the variable sector of the demand curve would be from 1848 USD/m

^{2}to 2166 USD/m

^{2}. The median of the variable section is 2007 USD/m

^{2}and the demand probability was 47.60% as a result of the linear regression analysis. The difference between the start and end points was 317 USD/m

^{2}and it is at 15.81% of the median, which is 2007 USD/m

^{2}. Therefore, the pre-sale prices will fluctuate within 15.81% of the median.

#### 3.3. Correlation Analysis

^{2}was surveyed for the apartments that are already sold. The study surveyed the actual prices of 70 apartments located in Gangnam-gu, Gangdong-gu, and Gangbuk-gu in Seoul that were traded in September 2019 based on the data disclosed by the Ministry of Land, Infrastructure, and Transport [15]. Also, the real estate information provided by N Website was used to survey the total level of apartment buildings, number of households, and number of complexes, which were the factors not included in the data provided by the Ministry of Land, Infrastructure, and Transport. Local underdeveloped area was surveyed by excerpting the study of Oh [11]. Oh classified the type of development level Seoul into developed, growing, depressed, and retarded as in Table 1 based on the housing survey results announced by the Ministry of Land, Infrastructure, and Transport in 2016.

^{2}) of apartments surveyed in the study and the descriptive statistics chart of each item are indicated in Table 2, and the distance to the nearest superstore from each apartment was surveyed by 0.1 km to survey the retail and amenities. In case of public transportation, the distance to the nearest station from each apartment was surveyed by 0.1 km and the walking distance to the nearest school was also surveyed by minute in case of education.

^{2}. PCC is a numerical representation of the linear correlation between two items, and the closer they are to 1 and −1, the more distinct they are.

^{2}with each item are as shown in Table 3.

^{2}. For that purpose, the study divided the minimum and maximum values of each item by 10 to rate each item between 1 and 10 (Appendix A).

^{2}for the comprehensive evaluation of each item, the correlation was 0.71 and significant, while the distribution of scores and unit prices per m

^{2}clearly showed that the sale prices per m

^{2}increased with greater comprehension scores as in Figure 5.

^{2}to derive correlation. Figure 6 indicates the comprehensive scores of the number of apartment complexes, local rates, and local development level and the dispersion of sale prices of apartments per m

^{2}. PCC using Equation (1) was 0.83 and higher than 0.71, which was the weighted value when all the items were applied.

^{2}, manifesting that the current study’s method of calculation was more reliable. Later, the correlation with the unit price per m

^{2}was derived by applying the weighted value of each item to the scores of the number of apartment complexes, local rates, and local development level as in Figure 7. Considering the importance of each item, the weighted values were 0.25, 0.50, and 0.25, respectively.

#### 3.4. Development of Pre-Sale Price Calculation Simulation Model

^{2}and not very different from 9879 USD per m

^{2}, the mean pre-sale price of apartments sold. In order to acquire more data, the study applied the mean sale price per m

^{2}, not the mean pre-sale price per m

^{2}, as the base price of apartments as the data of apartments sold at similar periods, not the apartments currently in the market, reflect prices not affected by market.

^{2}, the formulas in (2)–(4) were derived:

^{2}when the scores of all factors were 5.5 and Figure 12 indicates the sale prices derived from each time of simulation. The time of simulation is defined as one shift of graph by 1/100 from the start of fluctuation to the end of fluctuation when the start point was 0 and the end point was 100. In other words, n times of simulation simulates the shift of graph by 1/100 n times from the start of fluctuation to the end of fluctuation. The pre-sale price per m

^{2}increased gradually from the start point to the end point. Also, the simulation set all scores at 5.5, so the sale price per m

^{2}at 50th simulation 9879 USD, which was the mean sale price of Seoul.

^{2}multiplied by the pre-sale rate.

^{2}of apartments. The study surveyed 70 apartments sold in Seoul in September 2019, but the factors selected in the study were not significantly correlated with the sale price per m

^{2}except for a few. This is probably because the number of data gathered for the study were scarce, so a greater pool of data would be needed to draw the conclusion that many items affect the sale price per m

^{2}. The current study could not consider the special variables such as the speculators. Therefore, the simulation of the study could be improved if more factors affecting apartments are selected in future studies.

## 4. Conclusions

^{2}and the factors affecting the purchase of apartments were surveyed for 80 apartments traded in Seoul as of September 2019. After that, the correlation between each factor and the sale price per m

^{2}was derived using PCC (Pearson Correlation Coefficient). As a result, it was found that all factors except for the number of complexes, local rates, and local development level had no correlation with the sale price per m

^{2}. Therefore, the study applied the number of complexes, local rates, and local development level to calculate the sale prices of apartments. For that purpose, each factor was rated on a scale of 1 to 10 to tabulate the comprehensive score of each apartment. The current study applied the weighted value of each factor to readjust the comprehensive scores to derive the correlation with the sale price per m

^{2}to enhance the accuracy of study. PPC of weighted scores and sale prices per m

^{2}was 0.83, manifesting that the correlation between the two factors was very high.

^{2}, the fact that the weighted value of each factor was inaccurate, and the fact that there was not enough study was set the range of variable section. With follow-up studies to overcome the limitations, appropriate pre-sale prices will be calculated more accurately.

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A. Score Range by Item

Score | Total Number of Floors in Apartments | Number of Complexes | Retail and Amenities | Public Transportation | Educational Environment | Local Rates |
---|---|---|---|---|---|---|

10 | 62.6 ≤ A | 46 ≤ B | 0.1 ≤ C ≤ 0.26 | 0.2 ≤ D ≤ 0.33 | 1 ≤ E ≤ 2.6 | 30,572 ≤ F |

9 | 56.2 ≤ A < 62.6 | 41 ≤ B < 46 | 0.18 < C ≤ 0.26 | 0.33 < D ≤ 0.46 | 2.6 < E ≤ 4.2 | 27,763 ≤ F < 30,572 |

8 | 49.8 ≤ A < 56.2 | 36 ≤ B < 41 | 0.26 < C ≤ 0.34 | 0.46 < D ≤ 0.59 | 4.2 < E ≤ 5.8 | 24,954 ≤ F < 27,763 |

7 | 43.4 ≤ A < 49.8 | 31 ≤ B < 36 | 0.34 < C ≤ 0.42 | 0.59 < D ≤ 0.72 | 5.8 < E ≤ 7.4 | 22,146 ≤ F < 24,954 |

6 | 37.0 ≤ A < 43.4 | 26 ≤ B < 31 | 0.42 < C ≤ 0.50 | 0.72 < D ≤ 0.85 | 7.4 < E ≤ 9 | 19,337 ≤ F < 22,146 |

5 | 30.6 ≤ A < 37.0 | 21 ≤ B < 26 | 0.5 < C ≤ 0.58 | 0.85 < D ≤ 0.98 | 9 < E ≤ 10.6 | 16,528 ≤ F < 19,337 |

4 | 24.2 ≤ A < 30.6 | 16 ≤ B < 21 | 0.58 < C ≤ 0.66 | 0.98 < D ≤ 1.11 | 10.6 < E ≤ 12.2 | 13,719 ≤ F < 16,528 |

3 | 17.8 ≤ A < 24.2 | 11 ≤ B < 16 | 0.66 < C ≤ 0.74 | 1.11 < D ≤ 1.24 | 12.2 < E ≤ 13.8 | 10,911 ≤ F < 13,719 |

2 | 11.4 ≤ A < 17.8 | 6 ≤ B < 11 | 0.74 < C ≤ 0.82 | 1.24 < D ≤ 1.37 | 13.8 < E ≤ 15.4 | 8102 ≤ F < 10,911 |

1 | 5.0 ≤ A < 11.4 | 1 ≤ B < 6 | 0.82 < C | 1.37 < D | 15.4 < E | 5293 ≤ F < 8102 |

## References

- G20 New Housing Status, Korean Statistical Information Service (KOSIS). 2017. Available online: https://kosis.kr (accessed on 1 March 2020).
- Hong, L.H.; Lee, M.H. An Analysis of the Importance of Housing Indicators for the Evaluation of the Housing Sale. J. Resid. Environ. Inst. Korea
**2020**, 18, 319–332. [Google Scholar] [CrossRef] - Ezebilo, E.E. Evaluation of House Rent Prices and Their Affordability in Port Moresby, Papua New Guinea. Buildings
**2017**, 7, 114. [Google Scholar] [CrossRef] [Green Version] - Woo, A.; Joh, K.; Yu, C.Y. Who believes and why they believe: Individual perception of public housing and housing price depreciation. Cities
**2020**, 1–13. [Google Scholar] [CrossRef] - Son, S. A Simulation Model for Feasibility Analysis of Apartment Building Projects Using System Dynamics. Master’s Thesis, Kyung Hee University, Seoul, Korea, 2018. [Google Scholar]
- Kim, J.M.; Son, K.; Jang, J.; Son, S. Development of an income and cost simulation model for studio apartment using probabilistic estimation. J. Asian Archit. Build. Eng.
**2020**, 1–10. [Google Scholar] [CrossRef] - Huh, Y.K.; Hwang, B.G.; Lee, J.S. Feasibility Analysis Model for Developer-proposed Housing Projects in the Republic of Korea. J. Civ. Eng. Manag.
**2012**, 18, 345–355. [Google Scholar] [CrossRef] - Baik, M.S.; Shin, J.C. A Study on the Determinants of Initial Sales Rate for New Apartment Housing. J. Korean Urban Manag. Assoc.
**2011**, 24, 213–237. [Google Scholar] - Kim, Y.; Choi, S.; Yi, M.Y. Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices. Sustainability
**2020**, 12, 5679. [Google Scholar] [CrossRef] - Park, H.; Lee, D.; Kim, S. A Feasible Sale Price Assessment Model of Apartment Housing Units Considering Market Price and Buying Power. J. Asian Archit. Build. Eng.
**2015**, 15, 201–208. [Google Scholar] [CrossRef] [Green Version] - Oh, H. Housing Demand Estimation Model Using Under-Developedness Index. Master’s Thesis, Seoul Dankook University, Seoul, Korea, 2019. [Google Scholar]
- Song, S.; Zan, Y.; David, T.; Huan, Z. Uncertainty and New Apartment Price Setting: A Real Option Approach. Pac. Basin Financ. J.
**2015**, 35, 574–591. [Google Scholar] [CrossRef] - Jun, M.; Kim, H. Measuring the effect of greenbelt proximity on apartment rents in Seoul. Cities
**2017**, 62, 10–22. [Google Scholar] [CrossRef] - Park, J. PSM based Price Estimating for Local Mixed-Use Apartment Development. Korean J. Constr. Eng. Manag.
**2014**, 15, 86–94. [Google Scholar] [CrossRef] [Green Version] - Actual Transaction Price Data, Ministry of Land, Infrastructure and Transport Korea. 2019. Available online: http://rt.molit.go.kr/ (accessed on 1 March 2020).
- Kim, K.H.; Do, S.L.; Lee, D. Development of the Conceptual Model for Estimating Apartment Sales Price. Int. J. Recent Technol. Eng.
**2019**, 127–132. [Google Scholar]

**Figure 1.**Number of new housing in Korea, China, and Japan [1].

**Figure 7.**Scatter plot of total score correlated factors and sales prices per m

^{2}applying weight.

**Table 1.**The regional backwardness of the Seoul [15].

Development Level | Region in Seoul |
---|---|

Developed | Jongno-gu, Jung-gu, Yongsang-gu, Seongdong-gu, Dongdaemun-gu, Seongbuk-gu, Seodaemun-gu, Mapo-gu, Yangcheon-gu, Geumcheon-gu, Yeongdeungpo-gu, Dongjak-gu, Seocho-gu, Gangnam-gu, Songpa-gu |

Growing | Gwangjin-gu, Jungnang-gu, Dobong-gu, Eunpyeong-gu, Gangseo-gu, Guro-gu, Gwanak-gu, Gangdong-gu |

Depressed | Gangbuk-gu, Nowon-gu |

Retarded | - |

Influence Factor | Mean | Var. | S.D. | Max. | Min. |
---|---|---|---|---|---|

Unit prices (per m^{2}) of apartments(1000 won) | 11,601.00 | 50,565,600.70 | 7111 | 41,421.00 | 4190.00 |

Total level of apartment buildings | 18.00 | 109.54 | 10.00 | 69.00 | 5.00 |

Distance to station (km) | 0.58 | 0.17 | 0.41 | 2.20 | 0.10 |

Distance to Superstore (km) | 0.36 | 0.04 | 0.20 | 0.90 | 0.10 |

Number of complexes | 10.00 | 304.04 | 17.00 | 124.00 | 1.00 |

Distance to school (minutes) | 2.30 | 18.87 | 4.34 | 21.00 | 1.00 |

Influence Factor | PCC |
---|---|

Total number of floors in apartments | 0.11 |

Number of complexes | 0.62 |

Retail and amenities | −0.23 |

Public transportation | −0.11 |

Educational environment | −0.10 |

Local rates | 0.59 |

Local development level | 0.74 |

Apatments | The Actual Sale Prices | Result of Simulation | Error Rate (%) |
---|---|---|---|

A | 6535 | 6658 | −1.89 |

B | 7191 | 5803 | 19.31 |

C | 4941 | 4947 | −0.12 |

D | 8939 | 8826 | 1.27 |

E | 8034 | 6755 | 15.92 |

F | 6026 | 6755 | −12.09 |

G | 7274 | 8826 | −21.33 |

H | 12,357 | 10,898 | 11.81 |

I | 11,034 | 10,898 | 1.24 |

J | 9248 | 8826 | 4.56 |

K | 8878 | 10,537 | −18.69 |

L | 5416 | 6755 | −24.71 |

M | 6834 | 6755 | 1.17 |

S.D. (%) | 10.32 |

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**MDPI and ACS Style**

Kim, K.; Yun, J.; Kim, S.; Kim, D.Y.; Lee, D.
Development of Simulation Model for Proper Sales Price of Apartment House in Seoul. *Buildings* **2020**, *10*, 244.
https://doi.org/10.3390/buildings10120244

**AMA Style**

Kim K, Yun J, Kim S, Kim DY, Lee D.
Development of Simulation Model for Proper Sales Price of Apartment House in Seoul. *Buildings*. 2020; 10(12):244.
https://doi.org/10.3390/buildings10120244

**Chicago/Turabian Style**

Kim, Kihyuk, Jiyeong Yun, Sungjin Kim, Dae Young Kim, and Donghoon Lee.
2020. "Development of Simulation Model for Proper Sales Price of Apartment House in Seoul" *Buildings* 10, no. 12: 244.
https://doi.org/10.3390/buildings10120244