1# RECORD 59a1872eca8f8697a739527375ba1a22843bf5bd2# TITLE: The housing price dynamics and macroeconomic factors: Evidence of Taiwan housing market3# YEAR: 2025.0 VENUE: International Journal of Strategic Property Management DOI: 10.3846/ijspm.2025.246104# AUTHORS: Chih-Hsing Hung; Hui-Chuan Liu; Pei-Ran Su; Shyh-Weir Tzang5# PAGES: 21 CANDIDATE TABLE PAGES (1-based): [8, 9, 10, 11, 15, 16, 17, 18]6# ABSTRACT: The housing price in Taiwan has seen a sharp increase since 2020 after a long period of stability from 2011 to 2019. To provide a more granular analysis on this substantial rise of the housing prices since 2020 in Taiwan, we applied the hedonic pricing model to analyze the completed and presale residential housing across four types of houses: luxury, first-class, second-class and suite. By incorporating relevant macroeconomic variables from 2014 to 2022, we try to capture the demand of homebuyers across the three biggest cities in Taiwan. In the completed housing market, we find that the suite prices in Taipei and Taichung cities are more significantly affected by Taiwan’s continuing economic growth, whereas the suite prices in Kaohsiung city are hindered by its historically stagnant employment market. Notably, suite prices in Kaohsiung display greater sensitivity to construction cost changes and broader economic cycles compared to the suite prices in Taipei and Taichung. These findings highlight the unique dynamics of real estate markets in northern, central, and southern Taiwan, providing valuable insights for housing investors, prospective homebuyers in different regions and policymakers in their decision making.789===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) =====1011This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), 12which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.13Copyright © 2025 The Author(s). Published by Vilnius Gediminas Technical University14ISSN: 1648-715X / eISSN: 1648-917915INTERNATIONAL JOURNAL 16OF STRATEGIC PROPERTY 17MANAGEMENT18202519Volume 2920Issue 421Pages 245–26522https://doi.org/10.3846/ijspm.2025.2461023THE HOUSING PRICE DYNAMICS AND MACROECONOMIC FACTORS: 24EVIDENCE OF TAIWAN HOUSING MARKET25Chih-Hsing HUNG1, Hui-Chuan LIU2, Pei-Jia SU1, Shyh-Weir TZANG 3*261 Department of Money and Banking, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, R.O.C.272 College of Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, R.O.C.283 Department of Finance, Asia University, Taichung, Taiwan, R.O.C.29Article History: 30Abstract. The housing price in Taiwan has seen a sharp increase since 2020 after a long period of stability 31from 2011 to 2019. To provide a more granular analysis on this substantial rise of the housing prices since 322020 in Taiwan, we applied the hedonic pricing model to analyze the completed and presale residential hous-33ing across four types of houses: luxury, first-class, second-class and suite. By incorporating relevant macro-34economic variables from 2014 to 2022, we try to capture the demand of homebuyers across the three biggest 35cities in Taiwan. In the completed housing market, we find that the suite prices in Taipei and Taichung cities 36are more significantly affected by Taiwan’s continuing economic growth, whereas the suite prices in Kaohsiung 37city are hindered by its historically stagnant employment market. Notably, suite prices in Kaohsiung display 38greater sensitivity to construction cost changes and broader economic cycles compared to the suite prices in 39Taipei and Taichung. These findings highlight the unique dynamics of real estate markets in northern, central, 40and southern Taiwan, providing valuable insights for housing investors, prospective homebuyers in different 41regions and policymakers in their decision making. 42 ■received 13 January 202543 ■accepted 19 June 202544Keywords: hedonic price model, housing price index, macroeconomic variables, completed residential property, presale residential property.45 * Corresponding author. E-mail: swtzang@gmail.com 461. Introduction47According to the latest international statistics, Taiwan has 48one of the lowest fertility rates in the world. In 2024, Tai-49wan’s total fertility rate was estimated at 1.09 children per 50woman–well below the replacement level of 2.1 needed 51to maintain a stable population (worldpopulationreview.52com). This low fertility rate has led to population decline 53and aging, significantly affecting education, the labor mar-54ket, and economic development. Due to Taiwan’s declining 55population growth rate, people will increasingly move to 56and concentrate in several major cities–an almost certain 57trend for the future. As a result, to measure its impact on 58housing market, we focused on the three major cities in 59Taiwan to examine the changes in the housing market, as 60this can reflect shifts in real estate supply and demand 61and their impact on the value of different tiers of housing.62Based on the hedonic pricing models, this study also 63aims to highlight the recent sharp increase in 2020 in Taiwan 64housing prices by developing separate indices for existing 65and presale homes across Taiwan’s three largest cities–Taipei, 66Taichung, and Kaohsiung. By considering the macroeconom-67ic variables such as consumer price indices (CPI), construc-68tion cost indices (CCI), national income (NI), money supply, 69mortgage interest rates, and Taiwan Stock Exchange Market 70(TAIEX) performance, this research seeks to uncover distinct 71relationships between these factors and housing prices in 72different market segments. To better account for the slight 73changes in the period prior to 2020, we collected the data 74from 2014Q2 to 2022Q1. The findings will contribute to a 75deeper understanding of the dynamics of Taiwan’s real es-76tate market and provide timely information for policy mak-77ers to constrain the overheating housing market.78Real estate in Taiwan holds both economic and cul-79tural significance. Beyond serving as a hedge against infla-80tion for investors, property ownership symbolizes wealth 81and social status in Taiwanese society. According to Tai-82wan 2020 Population and Housing Census, the average 83rate of homeownership was 78.6%, of which Taipei city 84and Changhua county were 72% and 87%, the lowest and 85highest in Taiwan island, respectively. In this study, we se-86lect three cities, Taipei, Taichung, and Kaohsiung, as the 87focus of analysis by highlighting their geographical and 88economic differences, which can be better reflected in 89their respective real estate markets. Taipei is the traditional 90political center; Taichung is a hub for traditional, emerging, 91and electronics industries, and Kaohsiung is a center for 92traditional industries and tourism. 939495===== PAGE 8 (score 37, numeric-paren 0, table-words 5, var-words 8) =====969725298C.-H. Hung et al. The housing price dynamics and macroeconomic factors: Evidence of Taiwan housing market994.2. Descriptive statistics100By Chang et al. (2008), we screened the data by removing 101the transactions deviating from normal market prices, and 102special real estate registration data, followed by the consoli-103dation of standardized housing information for each cat-104egory. Tables 6 and 7 are the feature statistics according to 105the four categories of standard housing, i.e., luxury homes, 106the first-class housing, the second-class housing, and suite 107in Taipei city. Note that the data of 2016Q4 and 2019Q3 in 108Table 7 are not shown due to the incomplete data. 109Table 8 shows the basic statistics of Taipei city existing 110home price indices across four different categories of hous-111ing. We observed that the standard deviation of the luxury 112home and the second-class home price indices in Taipei are 113relatively high. This suggests greater fluctuation over the 114eight-year period. Moreover, the maximum and minimum 115values of the luxury home index exhibit the largest differ-116ence, indicating the greatest magnitude of variation.117Table 9 shows the basic statistics of Taichung city ex-118isting home price indices across four different categories 119of housing. We noted that the standard deviation of the 120suite index exhibits the highest variability, followed by the 121second-class housing index. The difference between the 122maximum and minimum values of the luxury home index 123is the smallest, indicating the least fluctuation. 124Table 10 shows the basic statistics of Kaohsiung city 125existing home price indices across four different catego-126ries of housing. We noted that the standard deviation and 127maximum value of the suite index are the highest com-128pared to other housing categories. Conversely, the stand-129ard deviation of the first-class index is the smallest, indi-130cating less fluctuation.131Table 6. The existing luxury and the first-class housing in Taipei city132Cat-133egory134Feature135Stats1362014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4137Luxury 138homes139total number 140of floors141mean14214.2414315.1614416.2214516.8914618.1514718.3514817.3614915.3115015.8815115.8815216.61153sampled floor154mean1558.141568.691578.701589.141599.371609.571619.791629.151639.481648.711659.73166registered total 167area168median 77.0116979.5417090.4617175.1517279.8517383.2717497.3117587.7817687.2917782.0317893.73179housing age 180(year)181median 2.851823.801832.601844.301854.601865.601873.051883.151893.901901.451914.30192parking lot193mean1941.611951.711961.991971.351981.681991.602001.882011.582021.622031.552041.88205location206mean2070.472080.632090.472100.742110.662120.672130.652140.592150.642160.732170.72218number of 219quarterly data 220# obs.22110822224922346322432822532622626322734822813022914623056231190232Feature233Stats2342017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3235total number 236of floors237mean23816.4123916.8524018.5024117.2324216.8624318.9424417.4324519.8124618.9724717.7924817.97249sampled floor250mean2519.562529.7825310.5925410.022559.1225612.542579.1725810.6625910.402609.7126110.58262registered total 263area264median 92.3926594.4826693.2626792.2926887.15269104.9927084.5127191.9227295.8027386.2227475.85275housing age 276(year)277median 4.452785.002793.602804.002812.352823.502833.802844.002855.302866.102874.00288parking lot289mean2901.832911.872921.872931.892941.582952.012961.782971.802981.832991.723001.58301location302mean3030.803040.483050.453060.503070.593080.683090.673100.693110.553120.633130.83314number of 315quarterly data 316# obs.3171763182113192053202183219032299323147324134325108326155327101328Feature329Stats3302019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1331total number 332of floors333mean33416.2633518.6233617.4833717.7533817.8033920.4934018.2734116.8334221.6334316.83344sampled floor345mean3469.6534710.3234810.2534910.263509.6535111.4635210.493539.5635413.453559.04356registered total 357area358median 83.3335974.2536085.5736180.4436289.3736395.2936476.3636574.52366129.5636769.13368housing age 369(year)370median 7.953715.303727.003737.103747.803756.653767.903777.603786.153798.30380parking lot381mean3821.463831.713841.613851.473861.693871.953881.483891.363902.643911.23392location393mean3940.333950.743960.533970.583980.563990.604000.734010.664020.784030.74404number of 405quarterly data 406# obs.407724089140918141020441120341231241320841424941564416297417418419===== PAGE 9 (score 42, numeric-paren 0, table-words 2, var-words 8) =====420421International Journal of Strategic Property Management, 2025, 29(4), 245–265422253423Cat-424egory425Feature426Stats4272014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4428The 429first-430class 431housing432total number 433of floors434mean43512.6943613.9643714.0843814.1543914.1744014.2144114.1144214.2644317.2244418.4944518.14446sampled floor447mean4489.224498.264508.184518.074528.174538.114548.414558.584569.9445710.2745810.23459registered total 460area461median 39.5946247.1146345.2046445.5146541.7146641.3746742.9246844.0346941.3547039.2547138.55472housing age 473(year)474median 6.404756.404766.104776.404786.704798.304808.104818.204825.204833.304843.40485parking lot486mean4870.634880.854890.784900.784910.664920.594930.604940.624950.764960.824970.77498location499mean5000.565010.425020.535030.545040.665050.645060.655070.665080.715090.735100.73511number of 512quarterly data 513# obs.514197515515516967517931518769519490520607521553522736523532524624525Feature526Stats5272017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3528total number 529of floors530mean53115.6653215.5853315.5853415.5753515.7353615.7853714.2053814.3553914.5354014.6454114.57542sampled floor543mean5448.995458.915468.805479.125489.405499.855508.355518.345528.535538.795548.90555registered total 556area557median 42.0855842.3255943.8056042.7956142.7856241.5156342.6756441.6156541.3756640.8656740.21568housing age 569(year)570median 7.405718.005728.105738.855749.005759.205768.755779.905788.905799.405809.80581parking lot582mean5830.715840.705850.735860.675870.655880.625890.705900.675910.645920.605930.59594location595mean5960.665970.585980.515990.516000.536010.606020.406030.446040.466050.496060.46607number of 608quarterly data 609# obs.610496611724612723613752614486615281616176617275618455619529620620621Feature622Stats6232019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1624total number 625of floors626mean62714.8362815.0062915.0863014.9163114.7663214.7863314.7463415.1263514.8463614.71637sampled floor638mean6399.186409.156418.856428.596438.526448.676458.536468.6164712.2264811.75649registered total 650area651median 40.1065240.2865340.7465442.0265541.5665640.6765739.6865839.4265940.9466040.63661housing age 662(year)663median 8.906648.906658.806669.8066710.1566810.8066911.3067013.8067113.7067213.80673parking lot674mean6750.646760.666770.706780.676790.686800.656810.656820.616830.676840.66685location686mean6870.456880.446890.466900.496910.506920.516930.496940.516950.576960.57697number of 698quarterly data 699# obs.70066470162270270470372970477070566270655970751970810087091099710Table 7. The existing second-class housing and suite in Taipei city711Category712Feature713Stats7142014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3715The sec-716ond-class 717housing718total number of floors mean71913.5172014.0072114.4472214.5772314.9772414.7772513.9972614.0772714.2572814.07729sampled floor730mean7317.707327.677338.077348.167358.047367.797377.607387.687397.587407.71741registered total area742median 55.8374350.0374448.3674549.2374648.8374748.1974847.6574948.2575047.7975148.16752housing age (year)753median 5.107545.807555.107565.807576.507586.407597.007608.357617.707626.60763parking lot764mean7651.007660.957670.917680.967691.027700.947710.897720.957730.937740.95775location776mean7770.357780.387790.337800.387810.417820.387830.407840.287850.267860.26787number of quarterly 788data 789# obs.79052479112857921875793192579417137951590796991797462798411799271800Feature801Stats8022017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2803total number of floors mean80414.3880514.2580614.1780714.2380814.5680914.4681014.3181114.5381214.3581314.06814sampled floor815mean8167.538177.728187.588197.538207.808217.908227.698237.878247.768257.90826registered total area827median 49.4882849.5582951.4983050.8783149.6883249.2883348.3283448.8983548.8483649.81837housing age (year)838median 8.958399.108409.108419.8084210.0084310.5084411.0084510.8084611.5084712.10848parking lot849mean8500.938510.878520.938530.928540.908550.888560.848570.868580.868590.87860End of Table 6861862863===== PAGE 10 (score 41, numeric-paren 0, table-words 2, var-words 9) =====864865254866C.-H. Hung et al. The housing price dynamics and macroeconomic factors: Evidence of Taiwan housing market867Table 8. Statistics of Taipei city existing home price indices across four different categories of housing*868Categories869Mean870Median871Max872Min873Std. dev.874Skewness875Kurtosis876J-B877Probability878# obs.879Luxury home880101.21 881100.99 882108.62 88397.79 8842.10 8851.29 8866.12 88721.848880.0000 88932890First-class89198.27 89298.23 893100.00 89496.57 8950.90 8960.04 8972.25 8980.758990.6867 90032901Second-class90299.98 90399.37 904105.63 90596.65 9062.43 9070.91 9082.79 9094.449100.1088 91132912Suite913100.42 914100.50 915103.67 91697.07 9171.99 918–0.07 9191.79 9201.989210.3724 92232923Note: *The base period to compute the index in this study is the second quarter of 2014 (2014Q2). 924Category925Feature926Stats9272014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3928location929mean9300.329310.349320.329330.359340.389350.399360.349370.359380.339390.32940number of quarterly 941data 942# obs.943480944477945548946639947547948428949467950417951626952486953Feature954Stats9552019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1956total number of floors mean95714.3195814.2995914.2096014.3796114.3096214.3596314.4896414.5596514.6596614.99967sampled floor968mean9697.939707.699717.599727.779737.599747.759757.949767.919778.129788.29979registered total area980median 47.2998145.7798245.6698347.4898448.7498548.2198644.6398743.3398843.7698944.09990housing age (year)991median 11.4599212.2099312.7099412.7099513.2599615.0099714.5599814.0099913.85100014.801001parking lot1002mean10030.8810040.8210050.8110060.8510070.8510080.8310090.8210100.8110110.8110120.721013location1014mean10150.3410160.4010170.3910180.3510190.3910200.4310210.4210220.3810230.3610240.451025number of quarterly 1026data 1027# obs.10284641029436103047710315751032522103343710344301035513103628810372411038Category1039Feature1040Stats10412014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q31042Suite1043total number of floors mean104412.82104513.25104613.43104713.26104813.42104913.60105013.67105113.78105216.17105317.211054sampled floor1055mean10568.3010577.8810587.6210597.8610608.5810618.4510628.5110638.5810648.8710659.181066registered total area1067median 17.43106818.04106918.76107019.51107119.51107219.41107320.20107420.07107519.43107619.921077housing age (year)1078median 18.45107917.45108014.45108116.50108218.70108318.70108418.95108518.8010868.7010877.801088parking lot1089mean10900.0710910.0910920.1410930.1310940.0710950.0610960.0810970.0910980.0810990.071100location1101mean11020.7811030.7511040.7411050.6911060.6211070.6211080.6211090.6211100.7411110.801112number of quarterly 1113data 1114# obs.11151961116304111725811182451119227112020511211901122175112320311241471125Feature1126Stats11272017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q21128total number of floors mean112914.43113013.89113113.90113216.10113316.69113414.94113514.29113613.99113714.13113814.131139sampled floor1140mean11418.7011428.5511438.4211449.2611459.7911469.1811478.5811488.1111498.6411508.521151registered total area1152median 19.55115319.43115419.43115519.07115619.58115719.85115819.27115919.63116019.21116119.471162housing age (year)1163median 19.30116413.60116520.50116610.3011679.30116811.50116914.55117020.85117120.50117220.401173parking lot1174mean11750.0811760.0911770.0611780.0411790.0411800.0411810.0411820.0511830.0911840.081185location1186mean11870.7811880.7911890.7211900.7811910.8111920.7511930.7311940.7011950.7311960.741197number of quarterly 1198data 1199# obs.12001691201163120217312032791204264120518712061801207122120812712092201210Feature1211Stats12122019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q11213total number of floors mean121415.10121515.08121614.12121714.31121814.38121914.28122014.24122114.25122214.13122314.151224sampled floor1225mean12269.4512279.5412289.1012299.3312309.1712318.8212328.6012338.6412348.5912358.621236registered total area1237median 19.70123819.56123918.20124016.27124115.50124215.50124315.35124415.36124515.36124615.251247housing age (year)1248median 14.90124922.25125023.30125122.60125223.80125324.40125424.90125525.20125625.10125726.201258parking lot1259mean12600.0912610.0912620.1212630.1012640.0712650.0612660.0812670.0812680.0712690.081270location1271mean12720.7012730.7012740.7712750.7312760.7112770.7512780.7512790.7612800.8012810.781282number of quarterly 1283data 1284# obs.12851851286182128724412883811289406129035812913281292397129356912945791295End of Table 7129612971298===== PAGE 11 (score 28, numeric-paren 0, table-words 4, var-words 5) =====12991300International Journal of Strategic Property Management, 2025, 29(4), 245–26513012551302Table 9. Statistics of Taichung city existing home price indices across four different categories of housing*1303Categories1304Mean1305Median1306Max1307Min1308Std. dev.1309Skewness1310Kurtosis1311J-B1312Probability1313# obs.1314Luxury home1315100.28 1316100.35 1317102.23 131896.45 13191.28 1320–0.93 13214.22 13226.56 13230.03761324321325First-class1326101.49 1327101.25 1328104.90 132998.26 13301.55 13310.28 13322.52 13330.73 13340.69351335321336Second-class1337102.69 1338102.71 1339109.96 134098.25 13412.42 13420.54 13434.08 13443.12 13450.21011346321347Suite1348108.72 1349108.04 1350163.73 135159.38 135218.03 13530.65 13546.18 135515.70 13560.00041357321358Note: *The base period to compute the index in this study is the second quarter of 2014 (2014Q2).1359Table 10. Statistics of Kaohsiung city existing home price indices across four different categories of housing*1360Categories1361Mean1362Median1363Max1364Min1365Std. dev.1366Skewness1367Kurtosis1368J-B1369Probability1370# obs.1371Luxury home1372102.48 1373102.48 1374106.79 137599.14 13761.73 13770.11 13782.83 13790.11 13800.9469 1381321382First-class1383100.65 1384100.54 1385103.81 138698.28 13871.17 13880.63 13893.62 13902.63 13910.2691 1392321393Second-class1394101.99 1395100.87 1396108.14 139799.84 13982.29 13991.22 14003.48 14018.24 14020.0162 1403321404Suite1405111.82 1406114.17 1407120.35 140897.44 14095.88 1410–0.76 14112.82 14123.12 14130.2105 1414321415Note: *The base period to compute the index in this study is the second quarter of 2014 (2014Q2).1416From Table 11, it is observed that the standard devia-1417tion of the construction cost index in Taichung city is rela-1418tively higher compared to other cities, indicating a larger 1419fluctuation range. Conversely, Kaohsiung city exhibits the 1420smallest fluctuation range in the construction cost index. 1421However, Kaohsiung city has the highest standard devia-1422tion in consumer price levels, suggesting the greatest de-1423gree of index variation.14244.3. Analysis of housing index1425Based on the results of the calculated residential price in-1426dices for various tiers over eight years in Taipei, Taichung, 1427and Kaohsiung using the house price index model (Chang 1428et al., 2008), Figures 3a to 5d present the existing housing 1429index charts for the three biggest cities in Taiwan. 1430Figures 3a to 3d contains four subplots of the four 1431tiers of housing index for the existing housing property. 1432Figure 3a shows the existing luxury residential index in 1433Table 11. Summary statistics of macro and construction factors across three cities*1434Variables1435Mean1436median1437Max1438Min1439Std. dev.1440Skewness1441Kurtosis1442J-B1443Probability1444# obs.1445Taipei CPI144697.52144797.561448101.31144994.1114501.8214510.2014522.4214530.6614540.71951455321456Taichung CPI145797.24145897.511459101.45146093.4814612.0114620.1814632.3214640.7814650.67591466321467Kaohsiung CPI146897.12146997.371470101.25147192.9414722.2014730.0214742.2214750.8014760.66941477321478Taipei CCI 147988.13148086.321481105.40148281.5414836.4514841.4114854.13148612.3414870.00211488321489Taichung CCI149086.85149184.651492105.99149380.0714947.0214951.4914964.27149713.9814980.00091499321500Kaohsiung CCI150188.31150286.451503104.25150481.3215056.2515061.2915073.7915089.7715090.00761510321511M1B151218021118 151316972986151425389297151513716369 339725815160.8015172.5215183.7515190.15361520321521M2152244103120152343445430152455006759152536443453 503301515260.5515272.4515282.0315290.36161530321531NI15324057689153339908631534506664315353469291153639547815370.8615382.9515393.9115400.14141541321542AMR15430.016515440.016315450.019915460.013515470.002115480.1215492.0415501.2915510.52551552321553TAIEX155411333155510518155618219155781811558298615591.3015603.3615619.1515620.01031563321564Note: *The base period to compute the index in this study is the second quarter of 2014 (2014Q2).1565Taipei, which exhibits significant fluctuations but generally 1566remains within a certain range over the long term. This 1567indicates periodic implementation of government policies 1568aimed at controlling luxury home prices. Figure 3d illus-1569trates the suite index in Taipei, which has shown a recent 1570upward trend. This suggests an increasing demand for 1571rental suites driven by the influx of population, particularly 1572from other regions. Consequently, there is also an increase 1573in demand from investors purchasing suites, leading to a 1574rise in the apartment price index. In view of the sensitiv-1575ity of the housing price index, the existing suite housing 1576index seems to be the most sensitive to the changes in 1577housing market, which showed a strong rebound as early 1578as 2020Q1, and then the first- and second-class housing 1579indices (Figures 3b and 3c) began to rise accordingly. The 1580existing suite housing index, therefore, should become an 1581important signal for the government housing policy mak-1582ers to monitor closely so as to make timely reaction curb 1583the speculation in housing market.158415851586===== PAGE 12 (score 3, numeric-paren 0, table-words 0, var-words 3) =====158715882561589C.-H. Hung et al. The housing price dynamics and macroeconomic factors: Evidence of Taiwan housing market1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610901611951612100161310516141101615103Q21616103Q41617104Q21618104Q41619105Q21620105Q41621106Q21622106Q41623107Q21624107Q41625108Q21626108Q41627109Q21628109Q41629110Q21630110Q41631a) Taipei existing luxury housing index163294163395163496163597163698163799163810016391011640103Q21641103Q41642104Q21643104Q41644105Q21645105Q41646106Q21647106Q41648107Q21649107Q41650108Q21651108Q41652109Q21653109Q41654110Q21655110Q41656b) Taipei existing first-class housing index165792165894165996166098166110016621021663104166410616651081666103Q21667103Q41668104Q21669104Q41670105Q21671105Q41672106Q21673106Q41674107Q21675107Q41676108Q21677108Q41678109Q21679109Q41680110Q21681110Q41682c) Taipei existing second-class housing index16839216849416859616869816871001688102168910416901061691103Q21692103Q41693104Q21694104Q41695105Q21696105Q41697106Q21698106Q41699107Q21700107Q41701108Q21702108Q41703109Q21704109Q41705110Q21706110Q41707d) Taipei existing suite housing index 1708Note: The X axis is using the Republic of China (ROC) calendar which is the method of numbering years currently used in 1709ROC (including the islands of Taiwan, Kinmen and Matsu). To find out the ROC year equivalent to any Gregorian calendar 1710year, we can add 1911 to the ROC year. For example: 103 + 1911 = 2014 year of the Gregorian calendar.1711Figure 3. Taipei existing housing indices across four tiers of housing from 2014Q2 to 2021Q41712Figures 4a to 4d depict the luxury, the first-class, the 1713second-class and suite housing indices of the existing 1714property in Taichung city. Over the past eight years, the 1715luxury, the first-class and the second-class housing indices 1716have all shown a trend of moderate growth. This indicates 1717that in recent years, an increasing number of people have 1718been moving to Taichung. According to statistics from the 1719Ministry of the Interior, despite the serious issue of de-1720clining birth rates in the past seven years, the population 1721of Taichung has increased by more than 100,000 people. 1722Consequently, there has been an increased demand for 1723housing, leading to a growth in price indices. In view of the 1724sensitivity of the housing price index, the existing luxury 1725and the first-class housing price indices seem to be more 1726sensitive to the housing market than the second- and suite 1727housing indices which are more stable in completed hous-1728ing market. 1729Figures 5a to 5d depict the luxury, the first-class, 1730the second-class and suite housing indices of the exist-1731ing property in Kaohsiung city. By the Figure 5d of the 1732suite housing index, it has consistently remained around 1733the mean value in recent years. This suggests that there 1734may be relatively fewer job opportunities in Kaohsiung 1735compared to Taipei. As a result, the increase in demand 1736for studio apartments or suites among office workers has 1737been relatively small, and there is also less demand from 1738investors in the suite market. Therefore, this price index 1739has shown a stable trend in recent years. On the other 1740hand, the existing second-class housing price has risen 1741much earlier than other indices and cities since 2019Q2, 1742indicating possible potential speculators from the market. 1743This can also be an important signal for local government 1744to monitor in the future. 1745Due to insufficient data on pre-sale housing, the cal-1746culation of the eight-year pre-sale housing price index is 1747not feasible. We only calculated and showed the results of 1748the pre-sale housing price indices for Taipei, Taichung and 1749Kaohsiung for a three-year period in Figures 6a–8d. 1750Figure 6a shows the luxury pre-sale housing index in 1751Taipei, reaching its peak in the Q1 of 2019, and entering 1752a consolidation phase after a decline in the Q2 of 2019. 1753Figure 6b illustrates the first-class pre-sale housing index 1754in Taipei, showing an upward trend reaching its peak in the 1755Q1 of 2022. Figure 6c represents the second-class presale 1756housing index in Taipei, reaching its highest point in the 1757Q2 of 2021 with the index consolidating before reaching 1758the peak. Figure 6d depicts the suite presale housing in-1759dex in Taipei that remains in a consolidation phase over 1760three years.1761Figure 7a depicts the luxury pre-sale housing index in 1762Taichung, reaching its peak in the Q1 of 2020. Following 1763a decline in the subsequent year, it then demonstrates an 1764upward trend. Figure 7b illustrates the first-class presale 1765housing index in Taichung, showing an upward trend over 176617671768===== PAGE 15 (score 31, numeric-paren 16, table-words 4, var-words 4) =====17691770International Journal of Strategic Property Management, 2025, 29(4), 245–26517712591772three years. Figures 7c and 7d represent the second-class 1773and suite housing indices in Taichung respectively, both 1774maintaining a consolidation phase over three years.1775Figure 8a shows the luxury pre-sale housing index in 1776Kaohsiung, reaching its lowest point in the Q2 of 2019, 1777followed by an upward trend. Figure 8b illustrates the first-1778class presale housing index in Kaohsiung, peaking in the 1779Q3 of 2019. After a decline the following year, it shows 1780an upward trend. Figure 8c represents the second-class 1781presale housing index in Kaohsiung, exhibiting an upward 1782trend over three years. Figure 8d depicts the suite presale 1783housing index in Kaohsiung, peaking in the Q2 of 2019, 1784declining until the Q4 of 2019, and then entering a con-1785solidation phase.17864.4. Unit root test1787In the context of unit root tests for variables such as price 1788indices and money supply, the test with a single intercept 1789term performs better than the test with both intercept and 1790trend terms (Elliott et al., 1996; Culver & Papell, 1997; Ley-1791bourne & Newbold, 1999; Ng & Perron, 2001). Therefore, 1792the study adopted the Augmented Dickey-Fuller (ADF) test 1793with a single intercept term to confirm whether the mac-1794roeconomic variables are stationary data. Table 12 above 1795presents the tests before and after differencing. The results 1796reveal that the first-order differencing CPI, money supply 1797(M2), average mortgage interest rates of the five major 1798banks, TAIEX, as well as the second-order differencing of 1799the construction cost index and national income in Taipei, 1800Taichung, and Kaohsiung, which reject the presence of a 1801unit root. Therefore, this study employs the differenced 1802macroeconomic variables for regression analysis.18034.5. Regression results1804The hses in Taiwan are categorized into the primary four 1805types: luxury homes, the first-class, the second-class and 1806suites as shown in the following:1807 1808 1809 1810 1811 1812 1813 181494181596181698181710018181021819104182010618211081822103Q21823103Q41824104Q21825104Q41826105Q21827105Q41828106Q21829106Q41830107Q21831107Q41832108Q21833108Q41834109Q21835109Q41836110Q21837110Q41838a) Kaohsiung presale luxury housing index18399418409618419818421001843102184410418451061846103Q21847103Q41848104Q21849104Q41850105Q21851105Q41852106Q21853106Q41854107Q21855107Q41856108Q21857108Q41858109Q21859109Q41860110Q21861110Q41862b) Kaohsiung presale first-class housing index1863941864961865981866100186710218681041869106187010818711101872103Q21873103Q41874104Q21875104Q41876105Q21877105Q41878106Q21879106Q41880107Q21881107Q41882108Q21883108Q41884109Q21885109Q41886110Q21887110Q41888c) Kaohsiung presale second-class housing 1889index189001891201892401893601894801895100189612018971401898103Q21899103Q41900104Q21901104Q41902105Q21903105Q41904106Q21905106Q41906107Q21907107Q41908108Q21909108Q41910109Q21911109Q41912110Q21913110Q41914d) Kaohsiung presale suite housing index1915Figure 8. Kaohsiung presale housing indices across four tiers of housing from 2019Q2 to 2022Q11916Table 12. Unit root test of macroeconomic variables using 1917ADF test1918Macroeconomic 1919variables1920Level1921First difference 1922t-stat1923Second 1924difference 1925t-stat1926Taipei CPI19270.53491928–7.2165***1929–7.2361***1930Taichung CPI19310.11651932–4.9485***1933–6.5187***1934Kaohsiung CPI1935–0.43351936–5.8720***1937–6.5726***1938Taipei CCI19390.72941940–1.96851941–6.2356***1942Taichung CCI19431.09661944–1.99041945–6.2546***1946Kaohsiung CCI19473.17521948–2.46511949–6.3955***1950M219513.20841952–3.2987**1953–6.3022***1954NI19553.36281956–2.22601957–7.3865***1958AMR1959–0.72221960–4.5898***1961–8.1799***1962TAIEX19630.49841964–6.0249***1965–11.6901***1966Note: ***and ** denote the unit root test with 1% and 5% significance 1967level, respectively. 196819691970===== PAGE 16 (score 38, numeric-paren 13, table-words 7, var-words 4) =====197119722601973C.-H. Hung et al. The housing price dynamics and macroeconomic factors: Evidence of Taiwan housing market1974 ■Luxury homes: high-income groups;1975 ■First-class: middle- and high-income groups;1976 ■Second-class: young couple or people earning the 1977average-income groups;1978 ■Suite: owner-occupiers and investors.1979After calculating the house price indices for each cat-1980egory using the index model, the regression analysis is 1981conducted with macroeconomic variables to examine the 1982positive or negative correlations and the extent of their 1983impact. Tables 13, 15 and 17 show the correlation coef-1984ficients between the house price indices of various levels 1985and macroeconomic variables for Taipei, Taichung, and 1986Kaohsiung over eight years. Tables 14, 16 and 18 present 1987the regression results of the house price indices of various 1988levels and macroeconomic indicators for Taipei, Taichung 1989and Kaohsiung over eight years.1990Existing property in Taipei city 1991 ■Taipei city luxury housing index for completed homes1992Model (1) in Table 14 shows the regression results of 1993the Taipei city luxury housing index for completed homes 1994and macroeconomic variables. There is no significant im-1995pact between the luxury housing index and macroeco-1996nomic variables, indicating that high-income groups are 1997not influenced by any macroeconomic variables when 1998purchasing homes.1999 ■Taipei city first-class housing index for completed 2000homes2001Model (2) in Table 14 shows the regression results 2002of the Taipei city first-class housing index for completed 2003homes and macroeconomic variables. The first-class hous-2004ing index is positively and significantly influenced by the 2005Taipei city CPI and the money supply, while it is negatively 2006and significantly influenced by mortgage rates. This indi-2007cates that when the market is active and interest rates are 2008lower, middle- and high-income groups are likely to use 2009the benefits from their investments to purchase homes 2010due to reduced interest costs.2011 ■Taipei city second-class housing index for completed 2012homes2013Model (3) in Table 14 shows the regression results of 2014the Taipei city second-class housing index for completed 2015homes and macroeconomic variables. There is no signifi-2016cant impact between the second-class housing index and 2017macroeconomic variables, indicating that young people 2018and average-income earners have an urgent need for 2019housing, and therefore changes in macroeconomic indi-2020cators are less likely to affect housing prices.2021Table 13. Correlation between the Taipei housing indices of existing property and macroeconomic variables2022Luxury2023First-class2024Second-class2025Suite2026Taipei CPI2027Taipei CCI M22028NI2029AMR2030TAIEX2031Luxury2032First-class20330.09082034Second-class20350.25752036–0.09252037Suite20380.052320390.5278***2040–0.29212041Taipei CPI2042–0.131420430.28502044–0.087020450.0550***2046Taipei CCI2047–0.251420480.19882049–0.049320500.3334*20510.02172052M220530.264520540.5050***2055–0.018720560.4651***2057–0.221720580.02052059NI20600.017520610.02242062–0.06962063–0.068820640.4962***2065–0.22772066–0.19642067AMR2068–0.00332069–0.098420700.08382071–0.091520720.060920730.058120740.16402075–0.18272076TAIEX20770.144420780.18692079–0.090320800.19682081–0.032320820.010320830.3438*20840.08172085–0.22942086Note: *** and * denote 1% and 10% significance level, respectively. 2087Table 14. Regression of Taipei housing index and macroeconomic variables across four housing categories2088Luxury2089First-class2090Second-class2091Suite2092(1)2093(2)2094(3)2095(4)2096Intercept2097100.5442***209897.2415***2099100.3078***210098.4118***2101Taipei CPI2102–0.335321030.6406***2104–0.347021050.62512106Taipei CCI2107–0.566721080.15022109–0.154121100.72642111M221121.29E-0621131.37E-06***2114–2.15E-0721152.81E-06***2116NI21173.76E-072118–2.62E-072119–2.21E-072120–1.34E-072121AMR21222.20272123–467.0859*2124398.67492125–921.47612126TAIEX21270.00012128–7.48E-052129–0.00012130–8.33E-052131adj R22132–0.07721330.3962134–0.22921350.2292136# obs.2137322138322139322140322141Note: *** and * denote 1% and 10% significance level, respectively. 214221432144===== PAGE 17 (score 40, numeric-paren 13, table-words 9, var-words 2) =====21452146International Journal of Strategic Property Management, 2025, 29(4), 245–26521472612148 ■Taipei city suite index for completed homes2149Model (4) in Table 14 shows the regression results of 2150the Taipei city suite index for completed homes and mac-2151roeconomic variables. The suite index is positively and sig-2152nificantly influenced by the Taipei city construction price 2153index and the money supply. This indicates that when 2154construction costs increase, housing prices also rise, lead-2155ing to an increased demand for renting suite apartments. 2156Consequently, investors are likely to use their investment 2157returns to purchase real estate.2158Existing property in Taichung city 2159 ■Taichung city luxury housing index for completed 2160homes2161Model (1) in Table 16 shows the regression results 2162of the Taichung city luxury housing index for completed 2163homes and macroeconomic variables. There is a significant 2164and positive impact between the luxury housing index and 2165both the Taichung city CPI and the money supply. This in-2166dicates that when the market is more active, capital more 2167easily flows into the luxury housing market, reflecting that 2168there is both residential and investment demand for luxury 2169homes in Taichung.2170 ■Taichung city first-class housing index for completed 2171homes2172Model (2) in Table 16 shows the regression results of 2173the Taichung city first-class housing index for completed 2174homes and macroeconomic variables. There is a significant 2175and positive impact between the first-class housing index 2176and the money supply. This suggests that middle- and 2177high-income groups are likely to use the benefits from 2178their investments to purchase higher-quality homes.2179 ■Taichung city second-class housing index for com-2180pleted homes2181Model (3) in Table 16 shows the regression results of 2182the Taichung city second-class housing index for com-2183pleted homes and macroeconomic variables. There is a 2184significant and positive relationship between the second-2185class housing index and the Taichung CPI, and a significant 2186and negative relationship with national income. This indi-2187cates that when prices rise and national income decreases, 2188young people and average-income earners tend to buy 2189houses below the average price. 2190 ■Taichung city suite index for completed homes2191Model (4) in Table 16 shows the regression results of 2192the Taichung city suite index for completed homes and 2193Table 15. Correlation between the Taichung housing indices of existing property and macroeconomic variables2194Luxury2195First-class2196Second-2197class2198Suite2199Taichung 2200CPI2201Taichung 2202CCI2203M22204NI2205AMR2206TAIEX2207Luxury2208First-class22090.5777***2210Second-class22110.4216***22120.4652**2213Suite22140.217822150.3188*22160.24222217Taichung CPI22180.294422190.146322200.281722210.31152222Taichung CCI22230.21752224–0.00562225–0.20262226–0.156222270.12872228M222290.3991***22300.5750***22310.189722320.07542233–0.13672234–0.15632235NI2236–0.11062237–0.09812238–0.3936**22390.167822400.21202241–0.01732242–0.19312243AMR2244–0.030922450.102322460.104622470.066922480.16362249–0.258922500.15162251–0.18032252TAIEX22530.153022540.24932255–0.12262256–0.04292257–0.101422580.177322590.347622600.08212261–0.23112262Note: ***, ** and * denote 1%, 5% and 10% significance level, respectively. 2263Table 16. Regression of Taichung housing index and macroeconomic variables across four housing categories2264Luxury2265First-class2266Second-class2267Suite2268(1)2269(2)2270(3)2271(4)2272Intercept227399.1064***2274100.1698***2275101.8984***2276102.2586***2277Taichung CPI22780.8018*22790.578222801.7055**22818.29962282Taichung CCI22830.271122840.05932285–0.59492286–2.64632287M222881.66E-06**22892.22E-06***22909.96E-0722915.39E-062292NI2293–4.84E-072294–2.05E-072295–3.64E-06**22965.95E-062297AMR2298–375.56972299–21.22522300–803.09442301–1354.892302TAIEX2303–6.20E-0523041.03E-042305–0.00022306–7.40E-042307adj R223080.19523090.22423100.2412311–0.0692312# obs.2313322314322315322316322317Note: ***, ** and * denote 1%, 5% and 10% significance level, respectively. 231823192320===== PAGE 18 (score 46, numeric-paren 19, table-words 8, var-words 4) =====232123222622323C.-H. Hung et al. The housing price dynamics and macroeconomic factors: Evidence of Taiwan housing market2324macroeconomic variables. There is no significant rela-2325tionship between the suite index and macroeconomic 2326variables. This suggests that Taichung has attracted many 2327residents from other counties and cities in recent years, 2328and these new residents have a stable demand for suite 2329apartments. Therefore, their demand for purchasing suite 2330apartments is less affected by macroeconomic changes.2331Existing property in Kaohsiung city 2332 ■Kaohsiung city luxury housing index for completed 2333homes2334Model (1) in Table 18 shows the regression results of 2335the Kaohsiung city luxury housing index for completed 2336homes and macroeconomic variables. There is no signifi-2337cant impact between the luxury housing index and macro-2338economic variables, indicating that the high-income group 2339in Kaohsiung city views real estate has a stable demand. 2340Therefore, changes in macroeconomic indicators do not 2341affect luxury housing prices.2342 ■Kaohsiung city first-class housing index for complet-2343ed homes2344Model (2) in Table 18 shows the regression results of 2345the Kaohsiung city first-class housing index for completed 2346homes and macroeconomic variables. There is a significant 2347and positive relationship between the first-class housing 2348index and both the Kaohsiung CPI and the money supply. 2349This suggests that when the economy is active, middle- 2350and high-income groups are likely to use the benefits from 2351their investments to purchase higher-quality homes.2352 ■Kaohsiung city second-class housing index for com-2353pleted homes2354Model (3) in Table 18 shows the regression results of 2355the Kaohsiung city second-class housing index for com-2356pleted homes and macroeconomic variables. There is a 2357significant and positive relationship between the second-2358class housing index and both the Kaohsiung CPI and the 2359money supply. This indicates that when young people and 2360average-income earners purchase homes, their limited 2361funds lead them to use the profits from their investments 2362to buy houses. Compared to the home-changing, middle- 2363and high-income groups, the average-income group is 2364more affected by the money supply.2365 ■Kaohsiung city suite index for completed homes2366Model (4) in Table 18 shows the regression results of 2367the Kaohsiung city suite index for completed homes and 2368macroeconomic variables. There is a significant and nega-2369Table 17. Correlation between the Kaohsiung housing indices of existing property and macroeconomic variables2370Luxury2371First-class2372Second-class2373Suite2374Kaohsiung 2375CPI2376Kaohsiung 2377CCI2378M22379NI2380AMR2381TAIEX2382Luxury2383First-class23840.4792***2385Second-class23860.6340***23870.7284***2388Suite23890.3965**23900.282223910.5046***2392Kaohsiung CPI23930.001923940.239023950.123323960.00802397Kaohsiung CCI23980.077523990.14142400–0.02272401–0.15322402–0.03672403M224040.3442*24050.4158**24060.6245***24070.24082408–0.232624090.07902410NI2411–0.25882412–0.13782413–0.06022414–0.208524150.3981**2416–0.4152**2417–0.19642418AMR24190.192724200.004024210.09372422–0.006624230.150324240.075724250.16402426–0.18272427TAIEX24280.133724290.176324300.226724310.3827**2432–0.103724330.082824340.3438*24350.08172436–0.22942437Note: ***, ** and * denote 1%, 5% and 10% significance level, respectively. 2438Table 18. Regression of Kaohsiung housing index and macroeconomic variables across four housing categories2439Luxury2440First-class2441Second-class2442Suite2443(1)2444(2)2445(3)2446(4)2447Intercept2448101.8043***244999.6302***245099.3691***2451110.9012***2452Kaohsiung CPI24530.416124540.7513**24551.0824*24561.75462457Kaohsiung CCI2458–0.125024590.02292460–0.24352461–1.8221**2462M224631.18E-0624641.39E-06**24653.99E-06***24661.01E-062467NI2468–1.67E-062469–9.76E-072470–9.46E-072471–8.11E-06**2472AMR2473323.94512474–434.5922475–369.98592476–171.71812477TAIEX24781.96E-0424793.73E-0524806.12E-0524812.36E-03**2482adj R22483–0.00724840.20524850.34924860.1922487# obs.2488322489322490322491322492Note: ***, ** and * denote 1%, 5% and 10% significance level, respectively.249324942495===== PAGE 19 (score 6, numeric-paren 0, table-words 0, var-words 6) =====24962497International Journal of Strategic Property Management, 2025, 29(4), 245–26524982632499tive relationship between the suite index and both the 2500Kaohsiung city CCI and national income, and a significant 2501and positive relationship with the stock price index. In a 2502city facing a prolonged economic depression, a decline 2503in national income is likely to shift homebuyers’ demand 2504from second-class housing to suites as well as studio 2505apartments, where prices will see an increase. However, 2506the inflexibility of rising rental prices of suites in Kaohsi-2507ung, coupled with the escalation in CCI, will in turn reduce 2508the demand of investors for the apartment studios. On the 2509other hand, the increase in stock market will also boost 2510the demand of investors for apartment studios, which is a 2511popular investment that can generate minimum required 2512return for investors in Kaohsiung. 25135. Conclusions2514This study compiles price indices for completed and pre-2515sale homes across four categories in the three biggest 2516cities in Taiwan, i.e., Taipei, Taichung, and Kaohsiung, and 2517examines the extent to which macroeconomic variables 2518impact the price indices of completed homes in each cat-2519egory. The results are summarized as follows.2520 ■Analysis of the completed home price index2521The real estate market price indices in Taiwan have 2522shown different trends. Although the luxury home price 2523index in Taipei fluctuates significantly, its long-term trends 2524indicate that prices remain within a certain range, suggest-2525ing that the government periodically implements policies 2526to control housing market prices. Due to the increasing 2527population of people moving to Taipei, the demand for 2528suites has also increased, which in turn drove up the de-2529mand from investors, resulting in a rising trend in the suite 2530price index in recent years. On the other hand, Taichung’s 2531growing population has led to increased housing demand, 2532resulting in a moderate growth trend in its housing price 2533index. In contrast, Kaohsiung has seen fewer people mov-2534ing there for work compared with Taipei and Taichung, 2535leading to lower demand for suites and subsequently a 2536more stable suite price index that stays around the aver-2537age value.2538 ■Analysis of the pre-sale home price index2539Between 2015 and 2018, the government implemented 2540policies to regulate housing prices, causing a 10% drop 2541in prices and transaction volumes. In 2020, the pandemic 2542prompted the government to lower the home purchase 2543interest rate to 1.35%. At the end of 2020, the central 2544bank implemented selective credit control policies to curb 2545housing prices, increasing the cost of home purchases for 2546the public and reducing the leverage used in real estate 2547investments to curb speculative behavior. After the regula-2548tory period from 2015 to 2018, the housing market began 2549to recover in 2019. The pre-sale luxury home indices in 2550Taipei and Taichung reached their peak in the first quarter 2551of 2020. In March 2020, the pandemic led to a drop in 2552the home purchase interest rate to 1.35%, combined with 2553rising construction costs, resulting in an upward trend in 2554Taipei’s second-class and suite pre-sale home indices in 2555the second quarter of 2020. The first-class pre-sale home 2556indices in Taipei and Taichung started to rise after the third 2557quarter of 2020. Due to the Kaohsiung government’s pro-2558motion of the Qiaotou Science Park and TSMC’s invest-2559ment announcement of a new Kaohsiung plant, Kaohsi-2560ung’s first-class pre-sale home index began to rise early 2561in the second quarter of 2020. To suppress housing prices, 2562the government introduced four rounds of credit control 2563and the Housing and Land Tax 2.0 policies starting to take 2564effect from the end of 2020. Taichung’s luxury home index 2565began to decline in the first quarter of 2021, while Tai-2566pei’s first and second-class home indices saw a significant 2567downward trend in the third quarter of 2021. Kaohsiung’s 2568pre-sale home market however remained largely unaf-2569fected.2570 ■Analysis of the impact of macroeconomic variables 2571on completed home indices by category2572In Taipei, high income homebuyers for the luxury 2573homes are not affected by economic changes. Middle to 2574high income homebuyers are more interested in buying 2575higher quality housing when the market interest rate is 2576low. Young people and average wage earners have an ur-2577gent demand for renting suites irrespective of economic 2578conditions. Despite the rising construction costs driving 2579up suite and home prices in recent years, the strong de-2580mand for suites has attracted more investors in buying 2581suite apartments.2582When the market is active, capital flows into Taichung’s 2583luxury home market, indicating that high-income groups 2584have both residential and investment demand for luxury 2585homes. Middle- and high-income groups also use invest-2586ment gains to purchase higher-quality homes in Taichung. 2587When prices rise and national income decreases, young 2588people and average-income earners tend to buy houses 2589below the average price. In recent years, Taichung has 2590attracted many residents from other counties and cities, 2591leading to a strong demand for suites from the migrating 2592population, making them less affected by macroeconomic 2593indicators.2594Compared to central and northern Taiwan, high-income 2595groups in Kaohsiung view real estate as a rigid demand, 2596making luxury home prices less affected by macroeco-2597nomic indicators. During active economic periods, young 2598people and average-income earners, who have limited 2599funds, use investment gains to purchase homes, making 2600them more affected by the money supply compared to 2601middle- and high-income groups who buy higher-quality 2602homes with their investment profits. When the stock mar-2603ket is active and national income decreases, the high cost 2604of entering the stock market drives investors to allocate 2605funds to suite apartments. As the construction price in-2606dex increases, suite investors seek to raise rental income. 2607However, southern homebuyers tend to be conservative, 2608preferring to purchase second-class homes rather than 2609renting suites or studio apartments, leading to decreased 2610investor demand for suites and studio apartments.2611