Assessing Real Estate Valuation Perceptions: A Case Study of Isparta Municipality Staff

Volume : 6 Issue : 2 Year : 2025 Pages : 103-121 e-ISSN : 2717-9230 103 ASSESSING REAL ESTATE VALUATION PERCEPTIONS: A CASE STUDY OF ISPARTA MUNICIPALITY STAFF Ali ÇELİKa, Süha ÇELİKKAYA b a Corresponding Author, Süleyman Demirel University, Graduate School of Social Sciences, [email protected], hFps://orcid.org/0000-0003-2734-3305. b Assoc. Prof. Dr., Süleyman Demirel University, Faculty of Economics and AdministraNve Sciences, Dept. of Economics, suhacelikkay[email protected].tr , hFps://orcid.org/0000-0002-4104-1680. ABSTRACT: Real estate valua*on holds significant importance for local governments and the real estate sector alike. For municipali*es, this process is par*cularly crucial, influencing a wide array of decisions from tax collec*on to land-use planning. This study inves*gates how personnel within a local government perceive real estate valua*on, with the aim of uncovering the key factors they believe shape property values. Such insights can empower municipali*es to formulate more informed and effec*ve real estate policies. To this end, a survey was conducted among 432 employees of Isparta Municipality. The analysis reveals that the par*cipants iden*fy several cri*cal criteria for valua*on: infrastructure and security, the property's loca*on, local popula*on density, access to essen*al services and educa*on, the socioeconomic status of the area, and proximity to entertainment and social facili*es. Among these, infrastructure and security emerged as the most important factor. Conversely, proximity to entertainment and social facili*es was perceived as the least influen*al criterion. Keywords: Real Estate Valua3on, Percep3on Level, Isparta Municipality. RECEIVED: 29 May 2025 ACCEPTED: 26 November 2025 DOI: hGps://doi.org/10.5281/zenodo.18057104 CITE Çelik, A., Çelikkaya, S., (2025). Assessing Real Estate Valua3on Percep3ons: A Case Study of Isparta Municipality Staff. European Journal of Digital Economy Research, 6(2), 103-121. hGps://doi.org/10.5281/zenodo.18057104 Research Paper Çelik, Çelikkaya 104 1. INTRODUCTION Real estate cons3tutes a cri3cal asset class for economic development and valua3on management. Its significance is amplified by the unique characteris3cs of real estate markets. Unlike more fluid markets such as equi3es, real estate o\en lacks con3nuous, transparent trading between buyers and sellers to establish clear prices. Consequently, decisions regarding real estate por^olios frequently rely on subjec3ve valua3on rather than observable market prices (Ade3loye and Eke, 2014). The valua3on of real estate—the process of es3ma3ng the value at which a property would trade on a specific date—serves as a fundamental ac3vity for a wide range of stakeholders. The purposes for which valua3ons are required are diverse, encompassing purchase and sale transac3ons, transfers, tax assessments, expropria3on, inheritance seGlements, and securing financing for investment (Pagourtzi et al., 2003). For municipali3es, in par3cular, accurate real estate valua3on is a cornerstone ac3vity with wide-ranging implica3ons, from ensuring fair tax collec3on and effec3ve land-use planning to guiding infrastructure development and urban regenera3on projects. The percep3on of how real estate valua3on is conducted holds considerable importance for both the real estate sector and local governments. While the process is influenced by numerous objec3ve factors, the subjec3ve understanding and interpreta3on of these factors by individuals involved can significantly impact the accuracy and reliability of the final valua3on. A sound, widely shared percep3on among stakeholders can foster confidence in valua3on outcomes, leading to more consistent and transparent market transac3ons. Conversely, if appraisers or key decision-makers operate with inaccurate or unclear percep3ons, the resul3ng valua3ons may be flawed, poten3ally leading to significant economic losses, misalloca3on of public resources, and legal disputes. For municipal personnel, whose roles may involve aspects of planning, taxa3on, permits, or public works, the level of understanding regarding valua3on principles can directly influence local governance. Personnel with a clearer percep3on can contribute to more accurate tax rolls, beGerinformed zoning decisions, and more efficient infrastructure projects. A lack of knowledge, however, could nega3vely affect municipal revenues through inaccurate assessments or lead to subop3mal project priori3za3on. Furthermore, public trust in local government can be bolstered by transparent and consistently applied valua3on prac3ces. This study aims to inves3gate these percep3ons at the local government level. Its primary objec3ve is to determine the percep3on levels regarding property valua3on among the employees of Isparta Municipality. The study seeks to iden3fy what these personnel consider to be the cri3cal factors and criteria in the valua3on process. The subsequent sec3on presents a review of relevant literature on real estate valua3on methods and related studies, followed by the methodology, analysis, and findings of the survey conducted with municipal staff. 2. LITERATURE REVIEW The academic and professional discourse on real estate valua3on is extensive, encompassing a wide range of methodologies, theore3cal cri3ques, and contextual applica3ons. This body of literature provides the founda3onal concepts and evolving debates per3nent to understanding valua3on percep3ons. A fundamental star3ng point is the examina3on of valua3on methods themselves. Pagourtzi et al. (2003) provide a concise overview, categorizing methods into tradi3onal and advanced groups. Tradi3onal methods include regression models, comparables, cost, income, profit, and contractor approaches. Advanced methods extend to ar3ficial neural networks (ANNs), the hedonic pricing method, spa3al analysis, fuzzy logic, and ARIMA models. This classifica3on underscores the progression from established techniques toward more complex, data-driven models. Several scholars have cri3qued and sought to refine tradi3onal valua3on frameworks for beGer capturing market reali3es. Born and Pyhrr (1994) argue that tradi3onal models, which o\en stabilize cash flow variables and assume efficient markets, fail to account for economic cycles. They propose a cycle valua3on model that integrates real estate supply and demand cycles, equilibrium price cycles, and property life cycles, demonstra3ng a significant impact on asset value compared to Assessing Real Estate Valua3on Percep3ons: A Case Study of Isparta Municipality Staff 105 trend-driven models. Mooya (2016) offers a broader theore3cal revision. Drawing on heterodox economic theory, new ins3tu3onal economics, and cri3cal realism, the author cri3ques standard neoclassical valua3on theory and proposes an alterna3ve framework to explain persistent market issues like price bubbles, anchoring bias, and valua3on under uncertainty. The integra3on of non-tradi3onal factors into valua3on has also been a key research area. Warren-Myers (2012) cri3cally analyzes the rela3onship between sustainability and market value, concluding that exis3ng research has yet to provide clear, norma3ve guidance for valuers to consistently incorporate sustainability aGributes into prac3ce. This highlights a gap between theore3cal recogni3on of sustainability's importance and its prac3cal applica3on in valua3on. The adop3on of advanced sta3s3cal and computa3onal techniques represents a significant trend in valua3on research. Worzala et al. (1995) applied neural network technology to residen3al sales price predic3on, comparing it to mul3ple regression analysis. Their findings, based on data from Fort Collins, Colorado, did not show neural networks to be a superior tool at that 3me and cau3oned about issues like result inconsistency across so\ware packages. Hoesli, Jani, and Bender (2006) employed Monte Carlo simula3ons to incorporate parameter uncertainty into valua3ons, using empirical data to construct probability distribu3ons. Their results showed that simula3on-derived values were generally close to hedonic values but were sensi3ve to assump3ons about long-term interest and growth rates. Fuzzy logic, introduced to valua3on by Bagnoli and Smith (1998), offers a way to handle qualita3ve aGributes. By allowing set membership values between 0 and 1, it enables the grading of nonnumeric factors, with the authors demonstra3ng its applica3on to an income-producing property. The compara3ve approach, a cornerstone of tradi3onal prac3ce, has also been subject to methodological enhancement. Cupal (2014) presents a detailed, advanced procedure for the compara3ve approach that incorporates sta3s3cal diagnos3cs and cluster analysis to improve the selec3on of comparable proper3es, addressing the challenge of market heterogeneity. Yeh and Hsu (2018) propose a "Quan3ta3ve Compara3ve Approach" that uses stepwise decomposi3on regression to es3mate objec3ve adjustment coefficients, aiming to overcome the subjec3vity inherent in the tradi3onal method. Their empirical tests showed this approach outperformed classical hedonic price models and neural networks in accuracy. Research has also delved into the behavioral and sen3ment-driven aspects of valua3on. Clayton, Ling, and Naranjo (2009) inves3gate the role of investor sen3ment in commercial real estate valua3on, finding that sen3ment influences pricing even a\er controlling for fundamentals like rent growth and risk premiums. Similarly, Wyman, Seldin, and Worzala (2011) call for a new or expanded paradigm that moves beyond efficient market theories, advoca3ng for models that consider the diverse actors and behaviors in real estate markets, drawing from complexity theory to explain value forma3on. The spa3al dimension of value has been profoundly impacted by Geographic Informa3on Systems (GIS). WyaG (1997) developed a GISbased property informa3on system to analyze spa3al influences, such as accessibility, on property value. By crea3ng value maps, the research demonstrated how a quan3ta3ve spa3al analysis could enhance an appraiser's understanding of local market factors and aid in selec3ng comparables, building on the founda3onal idea that loca3on is the primary value determinant (Goodall et al., 1972). A substan3al segment of the literature focuses specifically on the Turkish context, reflec3ng local regulatory, methodological, and market developments. Several master's theses have explored founda3onal and applied aspects. Öztürk (1985) conducted an early study on real estate valua3on. Later works examined specific methods: Sezgin (2010) inves3gated valua3on methods and alterna3ves for Treasury-owned real estate; Yılmaz (2019) emphasized core concepts, standards, and provided a sample applica3on using common approaches; and Üngüt (2017) explored the general principles and common prac3ces within the global and Turkish valua3on climate. Other Turkish studies have focused on specific models and factors. Khamrabaeva (2020) applied the Hedonic Price Model to determine factors affec3ng housing prices in Bursa, concluding that environmental factors were as influen3al as physical ones. Akyol (2017) conducted a study in Istanbul's Kağıthane district to determine a regional capitaliza3on rate for use in valua3on, Çelik, Çelikkaya 106 highligh3ng the area's development poten3al. Özbay (2010) introduced the Analy3cal Hierarchy Process (AHP) as a mul3-criteria decision-making method for use in real estate valua3on projects. The intersec3on of valua3on with urban policy and digi3za3on has also been explored. Karabaş (2010) examined the value-based method as an alterna3ve to area-based equality in urban transforma3on, analyzing the Bayrampaşa project. Değirmenciler (2008) addressed problems in Turkey's technical and legal valua3on infrastructure, recommending the adop3on of interna3onal standards and GIS-based value maps for effec3ve urban land management. More recently, Karadağ (2024) examined the impact of digitaliza3on, including big data, ar3ficial intelligence, and blockchain, on accelera3ng and improving the accuracy of the appraisal process. Further studies have considered the professional and organiza3onal context of valua3on. Şahin (2010) created a resource on appraisal methods and examined the training process and prac3ces of licensed firms in Ankara. Üreten (2007) outlined factors affec3ng value, methods used in developed countries, and analyzed the development of Real Estate Investment Trusts (REITs) in Turkey. Köse (2023) studied the rela3onship between organiza3onal culture and organiza3onal commitment within real estate appraisal companies, finding a posi3ve correla3on but no3ng complexi3es in the regression analysis. Finally, the cri3cal issue of incorpora3ng broader value concepts is addressed in the Nigerian context by Babawale and Oyalowo (2011). Their survey of property appraisers revealed a growing awareness of sustainability but a tendency to define it in social rather than economic or environmental terms, underscoring the global challenge of mainstreaming comprehensive sustainability into valua3on prac3ce. This comprehensive review illustrates the mul3faceted nature of real estate valua3on research, spanning from core methodological debates to the adop3on of new technologies and the integra3on of contextual socioeconomic factors—all of which inform the framework for inves3ga3ng professional percep3ons of the valua3on process. 3. A STUDY ON THE DETERMINATION OF THE PERCEPTION LEVELS OF ISPARTA MUNICIPALITY EMPLOYEES ABOUT REAL ESTATE APPRAISAL This sec3on presents the analysis and findings derived from a survey administered to personnel of Isparta Municipality. The survey was designed to gauge the employees' percep3on levels regarding real estate valua3on, aiming to iden3fy the criteria they consider influen3al in the valua3on process. 3.1. Research Methodology The primary objec3ve of this study is to determine the percep3on level of real estate valua3on among municipal staff and to iden3fy the key criteria they associate with this process. To achieve this, a survey method was employed to gather data directly from the employees of Isparta Municipality. The survey instrument consisted of two main parts. The first part collected demographic informa3on about the par3cipants. The second part u3lized a 5-point Likert scale (where 1 = Strongly Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, and 5 = Strongly Agree) to measure agreement with various statements concerning factors influencing real estate value. As of the survey date, the total popula3on (N) of personnel in Isparta Municipality was approximately 1950. The minimum required sample size was calculated using the formula 𝑛 = 𝑁𝑡!𝑝𝑞 𝑑!(𝑁 − 1) + 𝑡!𝑝𝑞 where *p* (probability of occurrence) and *q* (probability of non-occurrence) were set at 0.5, *t* was the theore3cal t-value for a 95% confidence level, and *d* was the margin of error (0.05). This calcula3on yielded a minimum sample size of approximately 321 individuals. To ensure robust representa3on, the survey was distributed via snowball sampling to a total of 432 municipal personnel. This final sample size exceeds the calculated minimum, thereby adequately represen3ng the popula3on and allowing for reliable sta3s3cal inference. The collected data were analyzed using the SPSS Sta3s3cs package program to generate the findings discussed in the following sec3ons. 3.2. Analysis of Research Data and Findings The data obtained from the 432 completed surveys are analyzed and presented below under thema3c subheadings. Assessing Real Estate Valua3on Percep3ons: A Case Study of Isparta Municipality Staff 107 3.3. Demographic StaSsScs The demographic profile of the respondents provides essen3al context for interpre3ng the percep3on data. The distribu3on of par3cipants by gender, age, educa3on, and workplace is summarized in Tables 1 through 4. Table 1. Distribu3on of Par3cipants by Gender Gender Frequency Percentage Woman 132 30.6% Male 300 69.4% Total 432 100.0% It is seen in Table 1 that the sample is predominantly male (69.4%), which may reflect the overall gender distribu3on within the municipality's workforce or the specific departments engaged with the survey. This demographic characteris3c is considered in subsequent hypothesis tes3ng to examine poten3al gender-based differences in percep3on. Table 2. Distribu3on of Par3cipants by Age Age Frequency Percentage 30 years old and under 76 17.6% 31-44 248 57.4% 45 years and older 108 25.0% Total 432 100.0% It is shown in Table 2 that the majority of respondents (57.4%) are within the 31-44 age bracket, represen3ng the core working-age group. This suggests that the data largely reflects the percep3ons of experienced, mid-career personnel. The distribu3ons of younger (≤30) and older (≥45) employees are roughly balanced, allowing for meaningful age-based comparisons. Table 3 depicts that educa3onal aGainment among respondents is high, with 77.8% holding at least an undergraduate degree. This indicates a generally well-educated sample, which could influence the complexity and nuance of their percep3ons regarding valua3on criteria. The presence of postgraduate (9.3%) and high school (22.2%) cohorts allows for examining the impact of educa3onal level on percep3ons. Table 3. Distribu3on of Par3cipants According to Educa3onal Status EducaNonal Status Frequency Percentage High school 96 22.2% Undergraduate 296 68.5% Postgraduate 40 9.3% Total 432 100.0% Table 4. Distribu3on of Par3cipants According to Where They Work Place of Work Frequency Percentage Town hall 312 72.2% Outside the City Hall 120 27.8% Total 432 100.0% As seen in Table 4, most par3cipants (72.2%) are based in the central municipal building. This group likely includes administra3ve, planning, and managerial staff whose work is more directly 3ed to policy and valua3on-related decisions. The 27.8% working outside the main hall may represent field personnel, whose prac3cal, on-theground experience could shape a different perspec3ve on the factors affec3ng property value. 3.4. DescripSve StaSsScs of the Scale The core of the analysis lies in the responses to the 30 Likert-scale statements. Table 5 presents the descrip3ve sta3s3cs (minimum, maximum, mean, standard devia3on) for each item, revealing which criteria par3cipants deem most and least important. Çelik, Çelikkaya 108 Table 5. Descrip3ve Sta3s3cs of Par3cipants' Responses to the Scale Statements Expressions included in the Scale Minimum Maximum Average Value Standard DeviaNon The view of the real estate is important. 1.00 5.00 3.9907 1.04200 The proximity of the property to the city center is important. 1.00 5.00 3.8519 1.08835 The proximity of the property to educaNonal insNtuNons is important. 1.00 5.00 3.9537 1.03205 The proximity of the property to health insNtuNons is important. 1.00 5.00 3.9352 1.04002 The proximity of the property to the entertainment center and shopping mall is important. 1.00 5.00 2.8981 1.19504 Technical infrastructure is important for real estate. 1.00 5.00 4.4722 0.76414 Road infrastructure is important for real estate. 1.00 5.00 4.5556 0.72520 Water infrastructure is important for real estate. 1.00 5.00 4.5556 0.76262 Electrical infrastructure is important for real estate. 1.00 5.00 4.6111 0.69246 Sewerage infrastructure is important for real estate. 1.00 5.00 4.5926 0.73415 Natural gas infrastructure is important for real estate. 1.00 5.00 4.5926 0.74668 Social infrastructure (social faciliNes, market areas) is important for real estate. 1.00 5.00 4.0370 0.92328 Proximity to recreaNon areas and parks around the property is important. 1.00 5.00 3.5463 1.17502 Proximity to the entertainment areas around the property is important. 1.00 5.00 2.7315 1.13666 It is important to have knowledge about infrastructure for real estate. 1.00 5.00 4.3426 0.76054 Security is important for real estate. 1.00 5.00 4.4722 0.78806 Proximity to transportaNon faciliNes is important for real estate. 1.00 5.00 4.4722 0.81127 The populaNon density around the Estate is important. 1.00 5.00 3.6481 1.10106 The populaNon growth around the Estate is significant. 1.00 5.00 3.5278 1.05934 Assessing Real Estate Valua3on Percep3ons: A Case Study of Isparta Municipality Staff 109 Expressions included in the Scale Minimum Maximum Average Value Standard DeviaNon The income level of the people around the Real Estate is important. 1.00 5.00 3.2222 1.22111 The number of rooms in the property is important. 1.00 5.00 4.1944 0.82263 The number of toilets and bathrooms in the property is important. 1.00 5.00 3.9815 1.01022 The facade of the property is important. 1.00 5.00 4.3519 0.78637 The sun exposure of the property is important. 1.00 5.00 4.4259 0.78505 The plan of the real estate is important. 1.00 5.00 4.4907 0.72727 The floor where the property is located is important. 1.00 5.00 4.2407 0.82727 It is important to have an elevator in the property. 1.00 5.00 4.2315 0.89967 The heaNng system of the property is important. 1.00 5.00 4.5278 0.72679 The real estate's risk cerNficate is important. 1.00 5.00 4.5926 0.74668 The age of the building is important. 1.00 5.00 4.4815 0.83409 Analysis of Table 5 reveals the statements with which par3cipants agreed most strongly. The mean scores provide a clear hierarchy of perceived importance. The criteria with the highest mean scores (all>4.55) are Electrical infrastructure (4.61), Sewerage infrastructure (4.59), Natural gas infrastructure (4.59), and the Real estate's risk cerSficate (4.59). This strongly indicates that municipal employees priori3ze fundamental, prac3cal, and legal-security aspects of a property. The emphasis on core u3li3es (electrical, water, sewer, gas) underscores a percep3on that a property's basic func3onality and connec3on to essen3al services are paramount to its value. The high ra3ng of the risk cer3ficate highlights a significant concern for safety and regulatory compliance, likely reflec3ng the professional context of the respondents who must consider legal and liability issues. Conversely, the criteria with the lowest mean scores are Proximity to entertainment areas (2.73) and Proximity to entertainment centers and shopping malls (2.90). This suggests that, within the framework of municipal du3es and personal percep3on, recrea3onal and lifestyle ameni3es are considered secondary or luxury factors that exert a weaker influence on property valua3on compared to infrastructural and safety fundamentals. This finding may reflect a pragma3c, rather than aspira3onal, view of what cons3tutes property value among public sector employees. Other notable high-scoring items include the HeaSng system (4.53), Building plan (4.49), and Security (4.47), reinforcing the focus on tangible building quality, design efficiency, and safety. Moderate importance is given to loca3onal factors like proximity to EducaSon (3.95) and Health insStuSons (3.94), and Property view (3.99), while socioeconomic factors like the Income level of neighbors (3.22) and PopulaSon growth (3.53) received rela3vely lower emphasis. Çelik, Çelikkaya 110 3.5. Analysis of Research Data and Findings Reliability of the Scale The internal consistency of the survey instrument—the degree to which all items measured the same underlying construct of valua3on percep3on—was assessed using Cronbach's Alpha coefficient. Table 6. Reliability Results of the Scale Cronbach's Alpha Value Scale Reliability 0.949 The calculated Cronbach's Alpha value of 0.949, as shown in Table 6, indicates an excep3onally high level of internal reliability for the 30-item scale. According to conven3onal psychometric standards, a value above 0.9 is considered excellent. This result strongly suggests that the items on the scale are consistently measuring a unified concept—the respondents' percep3ons of factors important to real estate valua3on—and that the collected data is highly reliable for subsequent analysis. Prior to conduc3ng parametric tests, it is necessary to examine whether the data follows a normal distribu3on. This was assessed by calcula3ng the skewness (which measures asymmetry) and kurtosis (which measures the "tailedness") of the distribu3on for each scale item. Table 7. Skewness and Kurtosis Values of Responses to the Expressions in the Scale Expressions included in the Scale Skewness Value Kurtosis Value The view of the real estate is important. -1.218 1.030 The proximity of the property to the city center is important. -.918 -0.030 The proximity of the property to educa9onal ins9tu9ons is important. -1.078 .612 The proximity of the property to health ins9tu9ons is important. -.914 0.080 The proximity of the property to the entertainment center and shopping mall is important. .165 -1.131 Technical infrastructure is important for real estate. -2.038 5.967 Road infrastructure is important for real estate. -2.615 9.516 Water infrastructure is important for real estate. -2.585 8.411 Electrical infrastructure is important for real estate. -2.847 11.426 Sewerage infrastructure is important for real estate. -2.727 9.634 Natural gas infrastructure is important for real estate. -2.671 8.993 Social infrastructure (social facili9es. market areas) is important for real estate. -1.140 1.298 Proximity to recrea9on areas and parks around the property is important. -.405 -.953 Proximity to the entertainment areas around the property is important. .503 -.770 It is important to have knowledge about infrastructure for real estate. -1.810 5.492 Security is important for real estate. -2.194 6.291 Proximity to transporta9on facili9es is important for real estate. -2.424 7.516 The popula9on density around the Estate is important. -.611 -.445 The popula9on growth around the Estate is significant. -.614 -.257 The income level of the people around the Real Estate is important. -.063 -1.154 The number of rooms in the property is important. -1.580 3.672 The number of toilets and bathrooms in the property is important. -1.157 .921 The facade of the property is important. -1.861 5.185 The sun exposure of the property is important. -2.066 5.918 The plan of the real estate is important. -2.367 8.487 The floor where the property is located is important. -1.561 3.514 It is important to have an elevator in the property. -1.471 2.366 The hea9ng system of the property is important. -2.501 9.017 The real estate's risk cer9ficate is important. -2.671 8.993 The age of the building is important. -2.351 6.696 Table 7 presents the skewness and kurtosis values for all 30 items. For a sample size greater than 100, Mayers (2013, p. 53) suggests that skewness and kurtosis values between -3.29 and +3.29 indicate an approxima3on of normality. The results reveal significant devia3ons. For instance, items related to core infrastructure (e.g., Electrical infrastructure: Skewness = -2.847, Kurtosis = 11.426) show high nega3ve skewness (meaning Assessing Real Estate Valua3on Percep3ons: A Case Study of Isparta Municipality Staff 111 responses are clustered toward the "Agree" end of the scale) and very high posi3ve kurtosis (indica3ng a sharply peaked distribu3on with heavy tails). Conversely, items like proximity to entertainment areas show near-zero skewness and nega3ve kurtosis, sugges3ng a flaGer distribu3on. Since the values for the majority of items fall outside the suggested ±3.29 range, it is concluded that the data does not conform to a normal distribu3on. This finding necessitates the use of nonparametric sta3s3cal tests (Mann-Whitney U, Kruskal-Wallis) for hypothesis tes3ng, as they do not require the assump3on of normality. To reduce the dimensionality of the 30 variables and iden3fy the underlying latent constructs guiding par3cipants' percep3ons, an Exploratory Factor Analysis (EFA) was performed. The suitability of the data for EFA was first confirmed. Table 8. Kaiser-Meyer-Olkin (KMO) and BartleG Test Results Test Value Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy 0.918 Test Value BartleF's Test of Sphericity Approx. Chi-Square = 12313.835 Significance (p-value) 0.000 As shown in Table 8, the KMO measure of 0.918 is classified as "marvelous," indica3ng that the paGerns of correla3on between items are compact and highly suitable for factor analysis. Furthermore, BartleG's Test of Sphericity was significant (p = 0.000 < 0.05), rejec3ng the null hypothesis that the correla3on matrix is an iden3ty matrix. This confirms that there are sufficient significant correla3ons among the variables to proceed with EFA. The factor analysis, using Principal Component Analysis with Varimax rota3on, extracted six factors with eigenvalues greater than 1. Some items with low communali3es or cross-loadings were excluded to achieve a clearer factor structure. The final solu3on, explaining 74.73% of the total variance, is presented in Table 9. Table 9. Exploratory Factor Analysis Results of the Scale Factors Factor Loading Eigenvalue Variance ExplanaNon Rate (%) Reliability FACTOR 1: Infrastructure and Security 9.860 33.999 0.964 Sewerage infrastructure is important for real estate. 0.911 Natural gas infrastructure is important for real estate. 0.900 Road infrastructure is important for real estate. 0.891 Electrical infrastructure is important for real estate. 0.889 Water infrastructure is important for real estate. 0.888 The plan of the real estate is important. 0.742 Technical infrastructure is important for real estate. 0.737 The heaNng system of the property is important. 0.720 Çelik, Çelikkaya 118 linking physical soundness with legal-compliance and safety, sugges3ng that for local government prac33oners, a property’s value is inseparable from its habitability and regulatory standing. This holis3c view aligns with calls in the literature for more integrated approaches to valua3on that account for complex, interlocking factors (Wyman et al., 2011), and it mirrors prac3cal concerns raised in the Turkish context about the necessity of robust technical and legal frameworks for effec3ve urban management (Değirmenciler, 2008; Karadağ, 2024). Conversely, the rela3vely low importance accorded to proximity to entertainment and social ameni3es presents a revealing counterpoint. While hedonic pricing models and consumer-facing market analyses o\en aGribute value to such lifestyle features, the municipal viewpoint appears more pragma3c and perhaps more aligned with long-term urban vitality than transient market trends. This divergence highlights a poten3al area for dialogue between municipal planners, who priori3ze essen3al services and infrastructure, and market appraisers, who must capture the full spectrum of buyer preferences. It underscores the importance of context in valua3on percep3on; what is paramount for a taxing authority or planning body may differ from what drives individual investment decisions. Furthermore, the strong sta3s3cal correla3on uncovered between the infrastructure/security factor and the loca3on factor suggests that municipal staff do not view these elements in isola3on. Instead, they perceive a synergis3c rela3onship where a prime loca3on's value is con3ngent upon—and amplified by— reliable infrastructure and safety, and vice-versa. This interconnected understanding supports the need for valua3on models that can capture these synergies, moving beyond addi3ve checklists to more systemic evalua3ons. The demographic analysis enriches this picture by demonstra3ng that percep3on is not monolithic. The finding that female employees place sta3s3cally greater emphasis on infrastructure, loca3on, and socioeconomic status may reflect differen3ated risk assessment, a more holis3c view of community stability, or dis3nct professional experiences. The male respondents' higher valua3on of entertainment ameni3es similarly invites further explora3on into the social and experien3al dimensions that shape professional judgment. The most striking and clear-cut demographic influence, however, is that of educa3on. The stepwise increase in the perceived importance of almost all valua3on factors with higher educa3onal aGainment is a powerful testament to the role of formal training in shaping a sophis3cated, comprehensive, and nuanced professional lens. This finding provides robust empirical support for the recurring emphasis in the literature on educa3on and training as a cornerstone for improving valua3on prac3ce (Şahin, 2010; Değirmenciler, 2008). It strongly suggests that eleva3ng the overall exper3se of municipal staff through con3nuous professional development would directly enhance the quality and consistency of valua3on-related decisions across the organiza3on. These insights culminate in significant prac3cal implica3ons and yield specific, ac3onable recommenda3ons for Isparta Municipality and similar governing bodies. The clear internal consensus on priority factors provides a unique opportunity to codify this expert judgment into ins3tu3onal prac3ce. It is recommended that the municipality develop a formal Municipal Valua3on Guideline Framework. This document would standardize the key factors for any valua3on with municipal relevance, assigning appropriate weight to infrastructure, security, loca3on, and access to essen3al services as iden3fied in this study. To move from percep3on to precise applica3on, this framework should advocate for the adop3on of Geographic Informa3on System (GIS) tools to create spa3al value maps. This would allow for the objec3ve analysis of loca3on, accessibility, and service proximity, transforming qualita3ve "local knowledge" into transparent, quan3fiable data, as demonstrated in prior research (WyaG, 1997). To directly address the powerful influence of educa3on level revealed in the study, a Comprehensive Professional Development Program should be ins3tuted. This program must be 3ered, offering founda3onal courses on valua3on principles, legal frameworks (e.g., risk cer3ficates), and tradi3onal methods to all relevant staff. For highly educated personnel and managers, advanced modules should delve into contemporary challenges such as incorpora3ng sustainability metrics into valua3on—a noted gap both globally (Warren-Myers, 2012) and in developing contexts (Babawale & Oyalowo, 2011)—and leveraging emerging technologies like big data and AI in appraisal processes (Karadağ, 2024). Training should also cover behavioral economic insights and market cycle analysis to Assessing Real Estate Valua3on Percep3ons: A Case Study of Isparta Municipality Staff 119 foster a cri3cal understanding of how sen3ment and external shocks can impact value (Born & Pyhrr, 1994; Clayton et al., 2009). Beyond formal guidelines and training, the municipality should foster interdisciplinary collabora3on by crea3ng cross-departmental commiGees for major projects involving valua3on. This would integrate the engineering perspec3ve on infrastructure, the planning perspec3ve on loca3on and zoning, and the financial perspec3ve on taxa3on and value, ensuring the holis3c percep3on evident in the survey data is reflected in holis3c decision-making. Finally, to ensure these measures remain effec3ve and relevant, the municipality should commit to ongoing research. This includes periodically replica3ng this percep3on study to track evolu3on and conduc3ng compara3ve research with privatesector appraisers, developers, and academics. Such ini3a3ves will benchmark municipal prac3ces, iden3fy emerging gaps, and foster an innova3ve culture that bridges the perceived divide between theore3cal valua3on models and the grounded, pragma3c needs of local governance. By implemen3ng these recommenda3ons, Isparta Municipality can systema3cally translate the valuable insights derived from its employees' percep3ons into a structured, transparent, and professionalized approach to real estate valua3on. This will not only enhance internal efficiency and consistency but also bolster public trust, support more equitable urban development, and strengthen the municipality’s capacity to steward sustainable economic growth. Furthermore, this study opens several avenues for meaningful future research that could deepen both academic understanding and prac3cal applica3on. A natural progression would be a compara3ve percep3on study between municipal employees in Isparta and those in other Turkish ci3es of varying sizes and economic profiles, such as a metropolitan center like Istanbul or a different regional capital. This would help determine whether the priori3za3on of infrastructure and security is a universal feature of municipal percep3on or if it varies with local market dynamics, disaster risks (e.g., earthquake zones), or administra3ve responsibili3es. Extending this comparison to private-sector real estate professionals—including licensed appraisers, real estate agents, and developers—would be invaluable. Such research could quan3ta3vely map the poten3al percep3on gaps between public administrators and market prac33oners, par3cularly regarding factors like entertainment ameni3es or sustainability, thereby iden3fying specific areas where municipal policies might benefit from closer market alignment or where professional training standards could be harmonized. Another cri3cal research opportunity lies in longitudinal study design. Replica3ng this survey following the implementa3on of targeted training programs or major policy shi\s (e.g., new zoning regula3ons or a na3onal building safety campaign) would provide empirical evidence on how professional percep3ons evolve in response to interven3on. This could measure the efficacy of training and policy communica3on strategies. Addi3onally, qualita3ve, in-depth interview or focus group studies with employees from different demographic and departmental backgrounds would richly complement the quan3ta3ve findings. Such research could uncover the underlying reasons why certain factors are priori3zed—exploring the narra3ves, experiences, and ins3tu3onal cultures that shape the sta3s3cal paGerns observed here, par3cularly regarding the gender and educa3on-based differences. Finally, future research should ac3vely explore the integra3on of technological and methodological advancements into the perceptual framework established here. For instance, studies could pilot and evaluate a decision-support system for municipal valua3ons that opera3onalizes the six factors iden3fied, perhaps using the Analy3cal Hierarchy Process (AHP) as a structuring method (Özbay, 2010). Research could also inves3gate the specific barriers and pathways to incorpora3ng sustainability metrics into municipal valua3on prac3ce, building on the global discourse (WarrenMyers, 2012) and ini3al awareness in developing contexts (Babawale & Oyalowo, 2011). Examining the perceived u3lity and trust in emerging tools like GIS-based value maps, automated valua3on models (AVMs), and blockchain for property records among municipal staff would provide crucial insights for the successful digital transforma3on of public asset management (Karadağ, 2024; WyaG, 1997). 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