1# RECORD ddac707c3201e8fd8ee39f4a8176310e5a6389762# TITLE: Potential impact of property management on the market value of dwellings in multi-owned housing3# YEAR: 2023.0 VENUE: Journal of Housing and the Built Environment DOI: 10.1007/s10901-023-10032-24# AUTHORS: Andrzej Muczyński5# PAGES: 21 CANDIDATE TABLE PAGES (1-based): [13, 15]6# ABSTRACT: The expanding role of property management services and the increasing share of multi-owned housing in many housing markets raises issues concerning the impact of such services on property value in this sector. The presented study investigated the potential impact of property management on the market value of dwellings in multi-owned housing developments. This impact was identified based on estimated implicit values of the extracted common property attributes dependent on property management services. An original research methodology tailored to the institutional arrangement in multi-owned housing developments was used, involving the estimation of hedonic regression models. Empirical research was conducted on representative samples of multi-owned housing developments in the housing market of the city of Olsztyn, Poland. The results showed that common property attributes such as building condition, building accessibility, cleanliness and orderliness, and land development significantly influenced dwelling prices and thus their market value. In extreme cases, the cumulative potential impact of property management services estimated on this basis can reach up to ca. 30% of the dwellings’ value. These findings have far-reaching policy and practical implications for urban development policies, including the built environment, land use and local housing, as well as for housing market investors and developers, homeowners associations, dwelling owners and property managers. Hence, it is recommended to expand research on this topic to other housing markets functioning in different institutional arrangements.789===== PAGE 1 (score 5, numeric-paren 0, table-words 0, var-words 5) =====1011ARTICLE12Received: 15 December 2021 / Accepted: 28 March 2023 / Published online: 25 April 202313© The Author(s) 202314 15 Andrzej Muczyński16amucz@uwm.edu.pl171 18Faculty of Geoengineering, Institute of Spatial Management and Geography, University of 19Warmia and Mazury in Olsztyn, Prawocheńskiego 15, 10-720 Olsztyn, Poland20Potential impact of property management on the market 21value of dwellings in multi-owned housing22Andrzej Muczyński123Journal of Housing and the Built Environment (2023) 38:2207–222724https://doi.org/10.1007/s10901-023-10032-225Abstract26The expanding role of property management services and the increasing share of multi-27owned housing in many housing markets raises issues concerning the impact of such 28services on property value in this sector. The presented study investigated the potential 29impact of property management on the market value of dwellings in multi-owned housing 30developments. This impact was identified based on estimated implicit values of the ex31tracted common property attributes dependent on property management services. An origi32nal research methodology tailored to the institutional arrangement in multi-owned housing 33developments was used, involving the estimation of hedonic regression models. Empirical 34research was conducted on representative samples of multi-owned housing developments 35in the housing market of the city of Olsztyn, Poland. The results showed that common 36property attributes such as building condition, building accessibility, cleanliness and or37derliness, and land development significantly influenced dwelling prices and thus their 38market value. In extreme cases, the cumulative potential impact of property management 39services estimated on this basis can reach up to ca. 30% of the dwellings’ value. These 40findings have far-reaching policy and practical implications for urban development poli41cies, including the built environment, land use and local housing, as well as for housing 42market investors and developers, homeowners associations, dwelling owners and property 43managers. Hence, it is recommended to expand research on this topic to other housing 44markets functioning in different institutional arrangements.45Keywords Property management · Property market value · Multi-owned housing · 46Hedonic pricing models · Poland471 3484950===== PAGE 13 (score 43, numeric-paren 19, table-words 8, var-words 2) =====5152Potential impact of property management on the market value of…53ever, this resulted from positive effects on prices of relatively expensive and small dwellings 54located in the peripheral city zones in newer (sub-developer) buildings being in better con55dition. Based on the results of the correlation analysis, the initial set of dwelling attributes as 56potential independent variables explaining their prices was reduced in all research samples 57by those variables for which both correlation coefficients proved to be insignificant at the 58accepted level of statistical significance. It should be noted that positively verified inde59pendent variables by the correlation analysis were correlated with each other much weaker 60than with the dependent variable. Thus, the principle that a regression model should include 61variables that are highly correlated with the response (dependent) variable and at the same 62time relatively weakly correlated with each other was fulfilled.63Using the basic analytical form of the regression function (1) and the reduced sets of 64explanatory variables, hedonic pricing models were estimated for the main sample S1 65(model M1) and the subsamples S2, S3 and S4 (models M2, M3 and M4, respectively) of 66MOH developments in the studied local housing market. Estimation of the structural param67eters of the models was performed using the ordinary least squares (OLS) technique. Since 68outlier observations can significantly bias the regression equations, the estimation proce69dures for each of the models were conducted in two steps. First, the originally estimated 70models were subjected to residuals analysis, which allowed to eliminate the most outlier 71observations from the originally formed observation sets S1-S4 based on the Cook’s dis72tance criterion (Cook & Weisberg, 1982). On the outlier-cleaned samples, the final regres73sion models M1-M4 were then estimated. These models showed a better fit to empirical 74data, higher parameter stability, and lower estimation errors compared to the originally esti75mated models. Standard statistical tests (following Aczel 2006; Goryl et al., 2009), such 76as Fisher-Snedecor (F) statistics, Student’s tests, and Durbin-Watson (DW) statistics, were 77used to statistically verify the finally estimated models. In addition, coefficients of deter78mination (R-squared and adjusted R-squared) and standard errors of estimation (Se) were 79calculated. The hedonic regression results in the estimated models are presented in Table 3.80Based on these results, it was stated that the estimated models meet standard statistical 81postulates as the regression coefficients obtained were statistically significant (at p < 0.05), 82the residuals were symmetric, random in nature and revealed no autocorrelation. Moreover, 83given complex dependencies and conditions concerning property transactions in the hous84ing market, it can be considered that the estimated models are characterized by the required 85(satisfying) adaptation to empirical data. This is evidenced by the relatively high values 86Table 2 Correlation results between dwelling price (P) as the dependent variable and the pre-selected dwelling87attributes (variables) in the sampled MOH developments. (source: own elaboration)88Research sample89S190S291S392S493Attribute94Variable95rS96τ97rS98τ99rS100τ101rS102τ103OL104X1105− 0.587106− 0.457107− 0.053*108− 0.044*109− 0.052*110− 0.041*111− 0.539112− 0.421113NL114X21150.080*1160.062*1170.5191180.4111190.262*1200.2121210.4361220.352123FL124X31250.142*1260.1091270.3341280.2581290.117*1300.090*1310.093*1320.078*133DA134X41350.3641360.2871370.210*1380.163*1390.4991400.3991410.2941420.227143BC144X51450.4411460.3571470.4441480.3561490.237*1500.1961510.5501520.446153BA154X61550.3861560.3071570.109*1580.090*1590.3071600.2461610.237*1620.187*163CO164X71650.5561660.4581670.4641680.3871690.4031700.3331710.6861720.568173LD174X81750.4181760.3371770.4281780.3501790.4131800.3371810.5731820.458183Note: (*) Correlation coefficients not significant at p < 0.051841 31852219186187188===== PAGE 14 (score 41, numeric-paren 0, table-words 15, var-words 0) =====189190A. Muczyński191of R-squared coefficients (63.4–73.2%) and low values of standard errors of estimation Se 192(0.0447 to 0.0997), which, in relative terms, indicate the accuracy of estimating dwelling 193values with these models ranges from ± 0.6% to ± 1.2%. The obtained results, therefore, 194confirmed the statistical validity of the estimated models, including the correctness of the 195choice of the analytical form of the regression function and the sets of explanatory variables 196that significantly influence the dependent variable. Thus, the models provide a reasonable 197Table 3 Hedonic regression estimation results for the dependent variable ln (Price). (source: own elaboration)198Variable199Regression coefficient200Standard error201t-statistic202p-value203Model M1204Intercept term205β02067.92402070.0638208124.15232090.0000210X1211β1212− 0.06842130.0121214− 5.64852150.0000216X3217β32180.02112190.00972202.17402210.0314222X4223β42240.05162250.01112264.65602270.0000228X5229β52300.05182310.01522323.39892330.0009234X6235β62360.03292370.01382382.37732390.0188240X7241β72420.04952430.00922445.37822450.0000246 F - statistic24740.224248DW - statistic2491.8404250R - squared2510.6345252Adjusted R - squared2530.6188254Se - standard error of estimation2550.0997256 N - number of observations257146258Model M2259Intercept term260β02617.80612620.0318263245.29072640.0000265X2266β22670.03472680.00952693.64672700.0007271X3272β32730.01902740.00762752.46872760.0178277X5278β52790.02172800.00982812.21292820.0325283X7284β72850.03542860.01222872.88452880.0062289X8290β82910.02932920.01232932.37692940.0222295 F - statistic29617.043297DW- statistic2981.7901299R - squared3000.6752301Adjusted R - squared3020.6355303Se - standard error of estimation3040.0470305 N - number of observations30647307Model M3308Intercept term309β03108.02913110.0406312197.89033130,0000314X4315β43160.04543170.01063184.28473190.0001320X6321β63220.03283230.01413242.33293250.0249326X7327β73280.04563290.01413303.24393310.0024332X8333β83340.03303350.01583362.07993370.0441338 F - statistic33918.0742340DW - statistic3411.7479342R - squared3430.6496344Adjusted R - squared3450.6136346Se - standard error of estimation3470.0460348 N - number of observations34946350Model M4351Intercept term352β03537.80713540.0568355137.47933560,0000357X2358β23590.05973600.02343612.55263620.0145363X4364β43650.05293660.01413673.75443680.0005369X5370β53710.04613720.01903732.43043740.0195375X7376β73770.05703780.02643792.16163800.0365381X8382β83830.04473840.01763852.53603860.0151387 F - statistic38822.367389DW - statistic3902.2045391R - squared3920.7317393Adjusted R - squared3940.6990395Se - standard error of estimation3960.0707397 N - number of observations398473991 34002220401402403===== PAGE 15 (score 14, numeric-paren 0, table-words 5, var-words 2) =====404405Potential impact of property management on the market value of…406tool to assess the influence of the individual explanatory variables ceteris paribus on the 407dwelling value in each sample of MOH developments.408Within substantive verification of the estimated models, their consistency with theoreti409cal and practical knowledge of price formation and market value determination of dwell410ings in MOH developments was assessed. It should be noted that the regression analysis in 411models M1, M3, and M4 resulted in the further reduction in the sets of explanatory variables 412previously verified by correlation analysis. In consequence, the following variables were 413eliminated: X8 (land development) in model M1, X2 (neighborhood location) and X5 (build414ing condition) in model M3, and X1 (overall location) in model M4, which can be justified 415by the relatively high correlation of variable X8 with variables X6 and X7 in sample S1 and 416the low correlation of variables X2 and X5 with the response variable in sample S3, or the 417location specificity of MOH buildings in sample S4. Secondary reduction of the explana418tory variables was, therefore, a logical extension of the correlation analysis. The strength 419and direction of the influence of extracted explanatory variables on the dependent variable 420were taken as the main criteria of substantive assessment of the models. This was carried 421out by assessing the consistency of signs and values of the regression coefficients located 422at individual variables. On the one hand, given the adopted way of variable description, 423the signs of the estimated regression coefficients were found to be consistent with a priori 424expectations. This is due to the fact the direction of influence of the individual explanatory 425variables on the dependent variable was generally positive. It should be emphasized that 426this regularity occurred for all common property variables (X5 -X8) in each of the estimated 427models. The only exception was variable X1 (overall location) in model M1, whose influ428ence proved to be negative again, which was expected given the correlation results and 429was explained above. On the other hand, taking into account the presented interpretation 430of regression coefficients in log-linear models, the values of the estimated coefficients were 431considered to be reasonable, as they were consistent with the theory of property valuation 432and corresponded to the practical experience of real estate appraisers on the analyzed hous433ing market. The estimated values of these coefficients indicate that the average strength 434of the influence of a unit change in particular common property variables ranged, ceteris 435paribus, respectively: 3.3% for X5(building condition), 3.8% for X6(building accessibility), 4364.7% for X7(cleanliness and orderliness), and 3.6% for X8 (land development). Noteworthy 437is the variable X7, which revealed a relatively high influence on the market value of dwell438ings in all estimated models, and which remains under the direct control of the property 439manager. To enable easier comparison of regression coefficients in the estimated models, 440they are listed separately in Table 4.441Regression coefficient442Model M1 Model 443M2444Model 445M3446Model 447M4448β04497.92404507.80614518.02914527.8071453β1454− 0.0684455-456-457-458β2459-4600.0347461-4620.0597463β34640.02114650.0190466-467-468β44690.0516470-4710.04544720.0529473β54740.05184750.0217476-4770.0461478β64790.0329480-4810.0328482-483β74840.04954850.03544860.04564870.0570488β8489-4900.02934910.03304920.0447493Table 4 Listing of regression co494efficients in the estimated mod495els. (source: own elaboration)496 4971 34982221499500501===== PAGE 16 (score 1, numeric-paren 0, table-words 0, var-words 1) =====502503A. Muczyński504The obtained findings have therefore confirmed that there are statistically significant 505positive relationships between observable common property attributes dependent on PM 506services and dwelling prices in MOH developments in the studied housing market. This 507makes it possible to conclude that the research hypothesis posed in the introduction was 508positively verified by empirical research. In other words, the extracted common property 509attributes were actually perceived by market participants as value drivers, as evidenced by 510their significant impact on dwelling prices in that market. Since these attributes are depen511dent on the intensity and quality of PM services, the estimated influence of them on dwell512ing prices identifies the potential impact of PM services on the market value of dwellings 513in MOH developments. This means that if the property manager (or owner) made decisions 514and actions to introduce the new and/or maintain the existing state of the considered com515mon property attributes as part of the PM services, it was statistically proven that this had 516an impact on the market value of dwellings in MOH developments in the studied housing 517market. This is a potential impact because for it to manifest itself, it is necessary to include 518active influence on individual common property attributes in the scope of PM services, 519and furthermore, it is difficult to conclusively determine to what extent managerial and to 520what extent executive activities have contributed to changes in these attributes, which, as a 521result, have had an impact on the market value of dwellings. The magnitude and structure 522of this impact are also determined by the estimated implicit values of the common property 523attributes. Determining the real impact of PM services on the market value of dwellings in 524the presented methodology requires further in-depth object-oriented research to assess the 525factual contribution of these services in shaping the value-creating attributes related to both 526common and individual properties.5275 Conclusions528Given the expanding role and importance of property management in many housing mar529kets, the impact of these services on property market value is undoubtedly a topical research 530problem of a global scope and a complex nature. This problem takes on special relevance 531in MOH developments, various forms of which are increasingly proliferating in different 532parts of the world, currently comprising a large proportion of the world’s housing stock. 533Although many authors have emphasized the impact of PM on the market value of a man534aged property, there have been relatively few efforts to measure it quantitatively, and there 535is still no consensus among researchers and professionals on the issue. This is particularly 536true for empirical studies in MOH developments, where the issue is largely ignored for 537various reasons. In an attempt to contribute to filling the gap, in this preliminary study, the 538potential impact of PM services on the market value of dwellings in MOH developments 539existing under Polish conditions using a selected local housing market was investigated. 540The methodological achievement of this paper is the conceptualization and operationaliza541tion of an original research methodology based on the triangulation of research methods 542tailored to the institutional arrangement in MOH developments. It assumes that PM services 543in such housing properties are mainly focused on the common property, but their potential 544impact on the property market value can only be manifested in dwelling prices since the 545common property, as such, cannot be traded on the market. Therefore, the said impact of 546PM services was estimated from dwelling prices, however, not directly, but in an indirect 5471 35482222549