The Impact of Non-Market Attributes on the Property Value

Abstract

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments.

1. Introduction

The Polish real estate market is constantly moving under the influence of the dynamic macroeconomic situation, new legal regulations, demographic changes and also the activities of market participants. Although market research on the Polish real estate market is conducted systematically by public institutions such as the Central Statistical Office (GUS) and the National Bank of Poland (NBP), it focuses mainly on the residential sector and price developments and is usually explored from a national perspective or at the level of voivodeships, remaining a gap in the data about granular segments of the market [1]. Extending the research to local markets can provide complementary information about the real estate market for interested parties including policymakers, developers, real estate agents and individuals.

International and European valuation standards, as well as legal regulations of many countries, recognise various types of a property value. According to the Property Management Act [2] containing the fundamental principles of real estate management in Poland, three main types of real estate value can be distinguished: market value, replacement value and cadastral value, and it is permissible to define other types of values, which are described in separate regulations. The concept of real estate values and their types was a subject of various studies [3,4,5,6,7] or as the element of cadastre analysis [8,9]. In this research, the examined type of property value is the market value, i.e., the most likely price at which a property can be traded on the free market.

Real estate appraisal is primarily based on the analysis of market features defined as characteristics that differentiate prices in the dataset of properties that constitute the basis for valuation [10]. Therefore, to estimate the market value of real estate, the appraiser has to identify relevant property characteristics (attributes) and determine their impact of the value. According to current regulations in Poland [11], such assessments can be carried out based on data on prices and the market attributes of similar real estate traded on the real estate market specified for the purpose of particular valuation. Examples of typical market attributes of apartments are location, the availability of public transport, the building condition, floor and finishing standard. The use of appropriate attributes and derivation of their impact on the estimated property values have been widely discussed in the literature. Numerous authors have examined factors determining the real estate value [12,13], the issue of key real estate features with the consideration of potential buyers’ preferences [14,15,16] and verified various methods to determinate weights of real estate attributes in shaping property values [17,18,19,20].

Nevertheless, residential needs of potential buyers constantly evolve, and, in the view of Polish cities’ growth, assessing how a quality of the surrounding environment affects a property value can provide valuable outcomes for real estate market participants and researchers. Other crucial qualities might cover characteristics linked with a micro-location, which are rarely considered in the comparative approach in real estate appraisal and market analysis. In this research, they are named as non-market factors and correspond to certain environmental conditions and micro-location aspects, which are not directly related to real estate markets but may have a significant impact on estimated values. The importance of environmental factors in property buyers’ choice has been the subject of various studies [21,22], indicating that certain aspects of a quality of the surrounding environment (e.g., noise emissions) have an influence on property values. Also, certain assets located in the surroundings of a property can have either positive or negative effects on its value. Examples of area uses leading to positive effects on the values of residential real estate in the neighbourhood are urban parks [23], schools [24] or water views [25], while brownfield [26] or industrial sites [27] constitute negative characteristics. Also, there might be micro-location aspects with the varied direction of impact; e.g., the effect of proximity to churches ranges from negative to insignificant and to positive [28]. Pioneering studies [29] have even been conducted to assess an impact of externalities such as proximity to 5G cell phone towers on residential property prices, where authors concluded that the introduction of such technology did not have a significant effect on real estate prices.

In this study, the main objective is to verify whether non-market attributes may affect the market value of residential apartments, estimate their impact and compare with results obtained for market attributes. To date, no research has been conducted on non-market factors that can affect market values of residential properties on the Polish local markets. The proposed examination has been carried out for the first time and constitutes only a proposal for an approach to this type of research, which may later be conducted on a wider scale in other property markets. For this purpose, the assessment has been carried out on both types of real estate features: market attributes and non-market attributes. The category of the market attributes includes location, public transport, floor, apartment condition, room layout, additional rooms, while the non-market attributes are air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings.

2. Materials and Methods

To achieve the research goal, a statistical analysis of the secondary market was carried out based on residential apartments located in the city of Rzeszów from the Zwięczyca district. Data were additionally enhanced by authors using Internet sources: Geoportal [30], Google Maps [31] and OpenStreetMap [32] to complete and enhance information about attributes required for this study.

2.1. Research Object

The scope of this research corresponds to apartments that are one of the most popular assets on the Polish real estate market. They are purchased not only to satisfy the buyer’s housing needs but also to invest in assets that gain in value over time and generate profits from rental incomes. According to Polish law, an apartment must meet the condition of being a self-contained dwelling unit, i.e., a room or a set of rooms separated from the rest of the building with permanent walls, which is intended to satisfy housing needs and also have a separate land and mortgage register number [33].

In this study, apartments were characterised by features divided by authors into two categories: market attributes (with two or three-point rating scale) and non-market attributes (with three-point rating scale).

2.2. Specification of Attributes

Market attributes refer to typical characteristics considered by real estate appraisers to differentiate property prices in datasets that constitute the base for valuation. In this study, the following market attributes were considered to illustrate the qualities of residential apartments: location, public transport, floor, apartment condition, room layout, additional rooms (

Table 1. Market attributes with their scale and description.

Attribute	Scale	Description
Location	favourable (3)	Location within 2 km from the city centre
	average (2)	Location above 2 km to 3 km from the city centre
	unfavourable (1)	Location above 3 km from the city centre
Public transport	favourable (3)	Public transport stops located less than 200 m
	average (2)	Public transport stops located from 200 m to 500 m
	unfavourable (1)	Public transport stops located more than 500 m
Floor	favourable (3)	First floor
	average (2)	Ground or last floor
	unfavourable (1)	Other than ground, first, last floor
Apartment condition	high (3)	High-quality, modern interior finishing, built year from 2020
	average (2)	Medium-quality interior finishing, built year from 2020
	poor (1)	Low-quality interior finishing or required renovation, built year before 2020
Room layout	flexible (2)	Separated rooms with various possibilities of using space
	inflexible (1)	Connected rooms or limited possibilities of using space
Additional rooms	yes (2)	Additional rooms (e.g., basement, storage) outside apartment
	no (1)	No additional rooms

).

Authors selected nine non-market factors that may have an impact on the market value of apartments: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Air quality or noise emissions are now attributes that may potentially affect the property value as potential buyers pay increasing attention to their health or so-called well-being issues. Proximity to green areas, rivers or lakes is primarily a recreational aspect but can also be linked with the attributes described above, namely, air quality and noise pollution. For young families or families with children, kindergartens and schools might be a major advantage. For older families or those thinking of renting, proximity to universities or secondary schools might be an important factor. Also, proximity to medical facilities, shopping centres or religious buildings may play a role in selecting an apartment. Other attributes that could be potentially considered by potential buyers, such as proximity to public administration offices, cultural centres or social events, were excluded from this analysis since they are generally located closer to the city centre.

Table 2. Non-market attributes with their scale and description.

Attribute	Scale	Description
Air quality	favourable (3)	Good or very good air quality
	average (2)	Average air quality
	unfavourable (1)	Poor or very poor air quality
Noise emissions	favourable (3)	Areas with low noise emissions
	average (2)	Areas with medium noise emissions
	unfavourable (1)	Areas with high noise emissions
Green areas	favourable (3)	Many green areas in the nearest surroundings
	average (2)	Some green areas in the nearest surroundings
	unfavourable (1)	Lack of green areas in the nearest surroundings
Rivers and water reservoirs	favourable (3)	River and water reservoir in the nearest surroundings
	average (2)	River or water reservoir in the nearest surroundings
	unfavourable (1)	No river or water reservoir in the nearest surroundings
Kindergartens and primary schools	favourable (3)	Kindergartens and primary schools located less than 300 m
	average (2)	Kindergartens and primary schools located from 300 m to 1 km
	unfavourable (1)	Kindergartens and primary schools located more than 1 km
Universities	favourable (3)	Universities located less than 1 km
	average (2)	Universities located from 1 km to 2 km
	unfavourable (1)	Universities located more than 2 km
Medical facilities	favourable (3)	Medical facilities located less than 0.5 km
	average (2)	Medical facilities located from 0.5 km to 2 km
	unfavourable (1)	Medical facilities located more than 2 km
Shopping centres	favourable (3)	Shopping centres located less than 1 km
	average (2)	Shopping centres located from 1 km to 3 km
	unfavourable (1)	Shopping centres located more than 3 km
Religious buildings	favourable (3)	Religious buildings located less than 0.5 km
	average (2)	Religious buildings located from 0.5 km to 2 km
	unfavourable (1)	Religious buildings located more than 2 km

outlines the scale of selected non-market attributes and their interpretation used in this research.

2.3. Geographical Area

This study is conducted based on the city Rzeszów, the largest city in south-eastern Poland with a population of around 200,000 people and an area of 129 km². The assessment of voivodeship capital cities indicated that Rzeszów is predicted to achieve the most significant increase in population between 2017 and 2030, i.e., by 7.1% [34,35]. To compare, the population in the capital city of Warsaw ranked as the 2nd position and was estimated to increase by 5.1%. The attractiveness of Rzeszów is reflected in the increasing demand and residential real estate prices. In December 2023, the average offer price of apartments was approx. PLN 8800 per 1 m² on the secondary market [36] and above PLN 9900 per 1 m² on the primary market [37], which indicates a growth by almost 15% and 16% YoY, respectively.

The city is divided into 22 cadastral districts (Figure 1). Zwięczyca (the number of cadastral district: 211) was chosen for this detailed research because of the largest number of transactions in the target (i.e., cleaned and filtered) database. Also, according to interviews with local real estate agents and potential apartment buyers, in 2024, Zwięczyca was indicated as one of the most attractive districts for living in Rzeszów.

2.4. Data Sample Derivation

The research data were sourced from the real estate price register provided by the City Office of Rzeszów. The raw dataset contained 3024 records with information about concluded real estate transactions (i.e., market type, real estate type, transaction price, property rights, share in the property right, address, cadastral district, land area number, function, the number of rooms, floor and usable area in m²). To extract a consistent sample for in-depth analysis, the data were cleaned by removing records with incomplete information and outliers were detected by visual inspections using box plots. Secondly, the dataset was narrowed down by introducing the following filters:

• Type of trade: free market;
• Selling party: individual;
• Buying party: individual;
• Real estate type: apartments constituting a separate property;
• Share in the property right: 1/1.

As a result of the data cleaning and filtering, the number of records was significantly reduced by more than 60%. To select the target data sample of similar transactions, each apartment was assigned to the district number based on its location (i.e., address), and records were further examined leading to the implementation of additional filters as follows:

• Range of transaction prices from PLN 6000 per 1 m² to PLN 12,500 per 1 m²: to ensure the elimination of atypical transactions that could adversely affect outcomes of further analyses.
• Living area from 40 m² to 70 m²: to focus on the most popular apartment sizes demanded by buyers (such area is preferred by families and by private investors).
• Apartments consisting of three rooms: to further standardise the target data sample, since they offer sufficient space for comfortable living and are one of the most attractive apartments on the real estate market.
• Secondary real estate market: to ensure the comparability of transaction prices and due to the larger number of transactions compared to the primary real estate market.
• Zwięczyca district: because of the largest number of remaining records in this district.

The target data sample contained 23 apartments with transaction dates from 10 January 2023 to 20 December 2023, gross transaction prices from PLN 345,000 to PLN 663,000, unit prices from 6349 PLN/m² to 12,157 PLN/m² and living areas from 40.29 m² to 63.90 m². Due to the significant growth of apartment offer prices on the secondary market (15% YoY in December 2023), an analogous development of transaction prices was assumed. As a result, the time adjustment of 1.25% a month was applied to unit prices in the data sample. All apartments were described with six market attributes and nine non-market attributes according to respective definitions presented in Table 1 and Table 2, which formed the basis for a correlation analysis. The spatial distribution of apartments in the final dataset is presented in Figure 2.

It is important to emphasise that there are discrepancies between market and non-market attributes in the property market, which this article positions and discusses in this section. However, it should be borne in mind that the figure of only 23 properties characterises these types of attributes only locally, for the district area analysed. Different values for the influence of market and non-market factors will be found in the other property markets analysed, and especially non-market factors will be more susceptible to localisation and market specificity.

2.5. Correlation Analysis

The applied approach to determine relationships between individual attributes and impacts on the property value is the analysis of correlation coefficients. In the statistical analysis of the real estate market, the property value is a dependent variable, whereas the specified attribute represents an independent variable. Relationships between a price and individual real estate attributes can be described using a multiple regression model or several independent models of a two-dimensional random variable. The parameter defining the mutual dependence of random variables is the covariance between a variable X and a variable Y, which is denoted by cov[X,Y]. Depending on the units of analysed random variables, a quantity cov[X,Y] may take different values. Therefore, the covariance value can be standardised using the values of standard deviations of both random variables in boundary distributions, i.e., σ[X] and σ[Y]. The standardised cov[X,Y] is a measure of linear interdependence of random variables X and Y, and it is called the Pearson’s total correlation coefficient [38].

Although the different types of correlation coefficients between variables X and Y (r_XY) can be applied in the data analysis [17], the most common type is the mentioned Pearson’s total correlation coefficient, which is defined by Formula (1):

$$r_{XY}={\displaystyle \frac{cov[X,Y]}{\sigma \left[X\right]\ast \sigma \left[Y\right]}}$$

(1)

The value of total correlation coefficients can be determined based on results from the sample (i.e., the database of apartments considered in the market analysis), according to the following alternative Formula (2):

$$r_{XY}={\displaystyle \frac{{\sum }_{i=1}^n(X_i-\overline{X}\left)\right(Y_i-\overline{Y})}{\sqrt{{{\sum }_{i=1}^n(X_i-\overline{X})}^2}\ast \sqrt{{{\sum }_{i=1}^n(Y_i-\overline{Y})}^2}}}={\displaystyle \frac{\frac{1}{n}{\sum }_{i=1}^n{X}_i{Y}_i-\overline{X}\overline{Y}}{\sigma \left[X\right]\ast \sigma \left[Y\right]}}$$

(2)

where $\overline{X}$, $\overline{Y}$ denote average values of variables X and Y, and σ[X], σ[Y] denote standard deviations of these variables.

Values of Pearson’s linear correlation coefficient range from −1 to 1 and measure both the direction and the strength of the relationship between considered variables. About the direction, positive coefficients indicate that, when the value of one variable increases, the value of the other variable also tends to rise. Negative coefficients represent cases when the value of one variable increases, the value of the other variable tends to decrease. When it comes to strength, the greater the absolute value of the Pearson’s correlation coefficient, the stronger the relationship. In this study, the following scale of the correlation strength was applied [38]:

• 0 ≤ |r| ≤ 0.3: week correlation;
• 0.3 ≤ |r| ≤ 0.6: average correlation;
• |r| > 0.6: strong correlation.

Based on the Pearson’s total correlation coefficient between the attribute X and the unit property price Y, the weight contribution k_XY can be calculated according to Formula (3) [38]:

$$k_{XY}={\displaystyle \frac{{r_{XY}}^2}{{\sum }_{i=1}^n{r_{XY}}^2}}$$

(3)

When it comes to a multidimensional random variable, it can be described similarly to the case of two explanatory variables, but, instead of a straight line in a two-dimensional coordinate system, there is a hyperplane in a higher dimensional space. Mathematically, the model with independent variables is expressed by Equation (4):

$${Y=a}_0+a_1{X}_1+a_2{X}_2+\cdots +a_k{X}_k$$

(4)

where Y is the explained (dependent) variable, X₁, X₂, …, X_k are explanatory (independent) variables, a₀ is the intercept term and a₁, …, a_k are coefficients of explanatory variables.

In multivariate regression, all attributes and unit property prices are analysed together: all correlation coefficients are defined based on the correlation matrix shown in Formula (5) and the elements of this matrix are the correlation coefficients between individual variables [38].

$$\left\{K\right\}=\left\{\begin{array}{ccccc}1& r_{01}& r_{02}& \dots & r_{0k}\\ r_{10}& 1& r_{12}& \dots & r_{1k}\\ \dots & \dots & \dots & \dots & \dots \\ r_{k0}& r_{k1}& r_{k2}& \dots & 1\end{array}\right\}$$

(5)

In this research, two correlation matrixes will be determined based on the target data sample: for market attributes and for non-market attributes. In the first step, the derived values will allow for detailed assessments of relationships between attributes and associated transaction prices. In the next step, the correlation coefficients will become a base for a calculation of weight contributions in shaping the market value of apartments.

3. Results

3.1. Correlations Within the Database of Market Attributes

To explore the relationships between attributes and also their relationship with the unit price, two matrixes of correlation coefficients were derived: for a 7-dimensional random variable (for market attributes) and for a 10-dimensional random variable (for non-market attributes).

The results for market attributes are presented in

Table 3. Correlation coefficient matrix for market attributes.

Attribute	Location	Public Transport	Floor	Apartment Condition	Room Layout	Additional Rooms	Price per 1 m2
Location	1
Public transport	0.51	1
Floor	0.40	0.22	1
Apartment condition	0.14	−0.02	−0.18	1
Room layout	0.11	0.12	−0.10	0.08	1
Additional rooms	0.22	0.14	0.33	0.09	−0.22	1
Price per 1 m2	0.41	0.21	0.34	0.34	0.25	0.26	1

, where the green colour means a positive correlation and red—a negative correlation between particular attributes. The weakest correlation (i.e., the minimum absolute value of correlation coefficients) was obtained for the pair of attributes: public transport—apartment condition (correlation of −0.02), while the strongest relation was identified for the pair of attributes: location—public transport (correlation of 0.51), which confirms the intuitive interpretation of real estate characteristics (e.g., the closer to the cite centre, the denser the public transport network). The correlation coefficients between particular market attributes and the transaction price per 1 m² ranges between 0.21 and 0.41, which denotes a positive direction and a weak or an average strength of correlations.

3.2. Correlations Within the Database of Non-Market Attributes

The correlation matrix of non-market attributes is presented in

Table 4. Correlation coefficient matrix for non-market attributes.

Attribute	Air Quality	Noise Emissions	Green Areas	Rivers and Reservoirs	Kindergartens and Primary Schools	Universities	Medical Facilities	Shopping Centres	Religious Buildings	Price per 1 m2
Air quality	1
Noise emissions	−0.20	1
Green areas	0.07	−0.51	1
Rivers and reservoirs	0.06	−0.36	0.93	1
Kindergartens and primary schools	−0.59	−0.01	0.41	0.33	1
Universities	−0.47	0.19	−0.51	−0.57	−0.13	1
Medical facilities	−0.64	0.13	−0.08	−0.20	0.38	0.75	1
Shopping centres	−0.69	0.03	0.07	−0.04	0.45	0.61	0.92	1
Religious buildings	−0.13	−0.36	0.64	0.48	0.64	−0.29	0.05	0.14	1
Price per 1 m2	0.29	−0.44	0.41	0.43	−0.15	−0.15	−0.09	−0.02	−0.13	1

, where correlation coefficients between the non-market attributes range from −0.69 to 0.93. The highest correlation (0.93) was obtained for the pair of attributes: green areas—rivers and water reservoirs, which confirms a close relationship between these features and suggests their simultaneous occurrence in the surroundings of a given property. Also, a high correlation (0.92) was observed for the pair: medical facilities—shopping centres, which is not surprising since the location of medical facilities in shopping centres or nearby is a commonly observed trend in Poland. Because of identified strong relationships between attributes, the authors decided to exclude one attribute from each pair of highly correlated features (i.e., green areas—rivers and water reservoir, medical facilities—shopping centres). Specifically, green areas and shopping centres were selected for removal due to their lower correlations with the price per 1 m² compared to the other attribute in the pair. The weakest correlation (0.03) was found in the pair of features: noise emissions—shopping centres, confirming almost no relation between these attributes. In comparison to the market attributes, there is a higher share of negative values in the matrix of correlation coefficients for non-market attributes. Identified inverse relationships between two attributes do not mean that such relationships are weak. The greatest negative correlation coefficient of −0.69 was obtained for the pair of attributes: air quality—shopping centres, indicating their strong relation with the inverse direction (i.e., air quality deteriorates significantly with the close proximity of shopping centres).

The correlation coefficients between particular non-market attributes and the transaction price per 1 m² range from −0.44 to 0.43. The top three weakest correlation coefficients were obtained for the attributes: shopping centres (−0.02), medical facilities (−0.09) and religious buildings (−0.13). Only three non-market attributes demonstrated an average strength of correlation with the unit price: noise emissions (−0.44), rivers and water reservoirs (0.43) and green areas (0.41). The correlation coefficient for noise emissions is negative, signifying an inverse relationship with the apartment price.

3.3. Impact of Real Estate Attributes on the Property Value

In order to examine the impact of attributes on the market value of apartments, the weights of considered attributes were calculated based on their correlation coefficients with transaction prices. The assessment of market attributes proved that the key attribute in determining the property value is location, which accounts for 29% of the impact (

Table 5. Calculation of weight contributions of market attributes.

Attribute	ri	ri2	ki
Location	0.41	0.1681	29%
Public transport	0.21	0.0441	8%
Floor	0.34	0.1156	20%
Apartment condition	0.34	0.1156	20%
Room layout	0.25	0.0625	11%
Additional rooms	0.26	0.0676	12%
Sum		0.5735	100%

). The lowest weight of approx. 8% was derived for the attribute public transport.

Similar calculation was performed for non-market attributes (

Table 6. Calculation of weight contributions of non-market attributes.

Attribute	ri	ri2	ki	ki′
Air quality	0.29	0.0841	16%	18%
Noise emissions	−0.44	0.1936	36%	42%
Rivers and water reservoirs	0.43	0.1849	35%	40%
Kindergartens and primary schools	−0.15	0.0225	3%	-
Universities	−0.15	0.0225	3%	-
Medical facilities	−0.09	0.0081	1%	-
Religious buildings	−0.13	0.0169	2%	-
Sum		0.5326	100%	100%
Sum of significant attributes		0.4626

). As explained in the previous section, green areas and shopping centres are excluded from the calculation of weight contributions due to the strong relationship with rivers and water reservoirs or medical facilities, respectively. In the analyses, it was assumed that attributes with the weight contribution k_i below 0.05 (5%) are irrelevant in explaining the property values. Following the reviewed articles [17], the limit was chosen arbitrarily. As a result, four attributes were eliminated from further calculations: kindergartens and primary schools, universities, medical facilities, religious buildings. After crossing out the irrelevant attributes, the key non-market attributes affecting the value of apartment were identified: noise emissions, rivers and water reservoirs and air quality with their weight contributions determined as 42%, 40% and 18%, respectively.

In the final stage, all attributes that may affect the market value of residential apartments were considered: both market and non-market. The elimination of attributes with the weight contributions below 5% resulted in eight real estate factors influencing the estimated value (

Table 7. Calculation of final weight contributions.

Attribute	ri	ri2	ki	ki′
Location	0.41	0.1681	15%	17%
Public transport	0.21	0.0441	3%	-
Floor	0.34	0.1156	10%	12%
Apartment condition	0.34	0.1156	10%	12%
Room layout	0.25	0.0625	6%	6%
Additional rooms	0.26	0.0676	6%	7%
Air quality	0.29	0.0841	8%	8%
Noise emissions	−0.44	0.1936	18%	20%
Rivers and water reservoirs	0.43	0.1849	17%	19%
Kindergartens and primary schools	−0.15	0.0225	2%	-
Universities	−0.15	0.0225	2%	-
Medical facilities	−0.09	0.0081	1%	-
Religious buildings	−0.13	0.0169	2%	-
Sum		1.1061	101% *	101% *
Sum of significant attributes		0.992

* Displayed weights do not sum up to 100% due to rounding.

). According to the results, the most important attributes are noise emissions (the weight contribution of 20%), rivers and water reservoirs (the weight contribution of 19%) and location (17%).

4. Discussion

The analysis of apartment transactions from the district of Zwięczyca in Rzeszów demonstrated varied strengths of correlations between considered attributes, as well as with the unit transaction prices. Also, many negative correlation coefficients, observed mainly for non-market attributes, point to an inverse relationships between particular features (i.e., an increase in one attribute is associated with a decrease in the other).

In the case of market attributes, the correlation between them is negative only for four pairs among seven attributes. The nature of negative relationships is more random and results from the construction of the database, rather than market relationships between attributes, the examples of negative correlation: floor—apartment condition or room layout—additional rooms.

None of non-market attributes is strongly correlated with the unit transaction price (i.e., values of correlation coefficients are below 0.6). The strongest relationship among the assessed non-market attributes was obtained for noise emissions (r = −0.44) with the inverse relation suggesting that apartments located in calmer areas can achieve higher market values, while a noise has a negative influence on the property value. Slightly lower correlation coefficients were observed for two attributes: rivers and water reservoirs (0.43) and green areas (r = 0.41), which denote that recreational aspects increase the attractiveness of apartments. According to the scale applied in this study, correlation coefficients indicate the average strength of relationships between mentioned non-market attributes and the unit transaction price. Almost half of non-market attributes have a negative correlation between each other; e.g., air quality has a negative impact on six among nine non-market attributes, noise emissions on five attributes, rivers and water reservoirs and universities on four attributes. Some of these negative correlations result from geographical distribution, e.g., air quality—shopping centres (−0.69) or green areas—noise emissions (−0.51). For instance, air quality is better further away from the city centre, where shopping centres are usually located. Some of negative correlations have a random character, which is the result of a database construction, e.g., religious buildings—universities (−0.29). Moreover, among nine non-market factors, six of them have a negative correlation with a price per 1 m², which also might be explained by a construction of the database, a relatively small number of observations or a local real estate market character.

In this study, correlations between non-market attributes themselves were also examined. High correlation coefficients were derived for two pairs of non-market features: green areas—rivers and water reservoirs (r = 0.93), medical facilities—shopping centres (r = 0.92). As a result, one attribute in each pair (i.e., green areas and shopping centres) was excluded from the calculation of weight contributions due to their lower correlation with a unit price.

The correlation analysis was followed by verification of the weights and their percentage impact on the market value of apartments. Because attributes with the calculated impact below 5% were rejected, it allowed us to specify the most important non-market attributes affecting the property value such as noise emissions (20%) and rivers and water reservoirs (19%). Based on the example of the Zwięczyca district in Rzeszów, the research conducted separately for market attributes and for non-market attributes, and jointly for both groups, evidenced that the surrounding environment and micro-location aspects can influence the market value of residential apartments.

5. Conclusions

In this study, the authors attempted to understand the relationships between various factors (i.e., market and non-market attributes) and also with a unit price of residential apartments. However, drawing general conclusions about the influence of considered attributes on residential real estate values is limited due to the narrow scope of dataset and the low number of observations in the sample. Nevertheless, this study highlights the potential importance of non-market attributes in creating the value of residential real estate, and, based on the results, the authors formulated the below findings, which may constitute a starting point for further, in-depth research:

• The analysis of the granular real estate market, narrowed down to one district, demonstrates that non-market attributes may play an important role in creating values of residential real estate, surpassing the materiality of market attributes, which are usually selected by appraisers in valuation processes.
• According to the obtained results for the Zwięczyca district in Rzeszów, the greatest influence on values of residential properties has a non-market factor: noise emissions.
• The findings provide an insight into externalities associated with a property location, which may support spatial planning approaches and local authorities decisions pertaining to priorities in urban planning.
• This study also strengthens awareness about the determinants of real estate prices, which can be applied by developers and real estate agents in understanding preferences of potential real estate buyers in terms of micro-location aspects.