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Article

Identification of Differences in the Seasonality of the Developer and Individual Housing Market as a Basis for Its Sustainable Development

by
Paweł Frącz
1,
Ireneusz Dąbrowski
2,
Daria Wotzka
3,
Dariusz Zmarzły
3 and
Łukasz Mach
4,*
1
Faculty of Economics, Opole University, 45-040 Opole, Poland
2
Collegium of Management and Finance, Warsaw School of Economics, 02-554 Warszawa, Poland
3
Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
4
Faculty of Economics and Management, Opole University of Technology, 45-758 Opole, Poland
*
Author to whom correspondence should be addressed.
Buildings 2023, 13(2), 316; https://doi.org/10.3390/buildings13020316
Submission received: 21 December 2022 / Revised: 17 January 2023 / Accepted: 18 January 2023 / Published: 20 January 2023
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Abstract

:
This article identifies seasonality profiles for three stages of housing construction. The profiles were determined as average monthly values obtained for permits issued for the construction of new apartments, for apartments whose construction has begun, and for apartments put into operation. The research process showed that there are differences in seasonality profiles between investments carried out by individual investors and those who build apartments for sale or rent. The research clearly showed that the development of reports and analyses for the housing market should include a breakdown of the market for the activities of individual developers as well as those operating as investments. Unfortunately, at present, reports on the real estate market are developed in total terms, which significantly reduces their utilitarian application. Taking into account the recommendations of the research will allow for sustainable development of the housing market in such a way that the market will strengthen its resilience to the occurrence of cyclical fluctuations, among other things.

1. Introduction

Housing construction is an area that has a significant impact on maintaining financial as well as macroeconomic stability in the economy. The significance of the impact of the housing construction market on economic parameters makes the analysis of periodic fluctuations of the phenomena in it essential for creating and conducting appropriate market activities. To model the components of the housing construction process, it was considered whether the realization over time of the phenomena under study is characterized by systematic changes on an annual basis. Identification of annual variability allows analysis of the seasonal and periodic fluctuations taking place in the housing construction market. Parameterization of the fluctuations taking place is important in the process of implementation of macro prudential policy and financial stability, as analysis of their rapidly varying courses allows for optimal implementation of economic policy. Understanding seasonal fluctuations will also allow those operating in this market to improve their efficiency in the decision-making process. Taking into account the significant impact of the real estate market on the economy and on the demand and supply side of the market, knowledge of its seasonal fluctuations will allow, among other things, the development of the residential real estate market in terms of sustainable development. This will result in the simultaneous impact of this market (according to the definition of sustainable development) on the economic, social, and environmental dimensions [1,2,3,4,5,6]. Accurate knowledge of seasonal fluctuations, or lack thereof, affects the sustainability of all elements of the urban environment (including real estate) and is very much needed in decision-making processes. Knowledge of seasonal fluctuations influences the sustainable value and can reflect not only economic issues (reflected directly by the value of real estate) but can also go beyond its knowledge in a broader sense to include sustainability issues (social, political, environmental directions) at the same level [7,8]. It should also be noted that theories suggesting how sustainable development should affect value have been interpreted in the form of scenario (case) studies and cost studies, which are not applicable to the practice of valuation and market value assessment [9]. Taking into account the criterion of market maturity and economic competitiveness, the definition of a sustainable market in the real estate sector indicates its maturity through well-established values and transactions and, in terms of the region, confirmation that it has achieved a sufficient level of economic competitiveness and resilience to cope with go-go cycles over time [10].
In this study, the identification of seasonal fluctuations occurring in the housing market was carried out for three stages of the housing construction process, which included the number of building permits issued, the number of units under construction, and the number of new units completed. The market for individual investors, as well as the market for development activities, was studied. Individual investors in this research are identified as people who build apartments for their own use, while developers, by definition, erect apartments for sale or rent. It should also be noted that individual investors do not treat the realization of an undertaking such as erecting an apartment as an investment project but carry it out to satisfy their own housing needs. Developers, on the other hand, treat the process of apartment construction exclusively in investment terms. Moreover, the research was conducted only in the primary housing market.
In the conducted research, seasonality profiles were determined, which provided an answer to the question, what is the nature of harmonic variability in terms of seasonality and periodicity of the studied components of the construction process? In addition, the conducted research clearly showed that the individual and developer housing markets, in many cases, are characterized by different characteristics of seasonal runs. The different profile of seasonal fluctuations makes it necessary to create different decision-making models for these markets. The knowledge of seasonal profiles and the ability to indicate the differences between the studied markets will allow the development of different development strategies for the development market as well as the individual market. Accurate identification of differences in the markets in question will undoubtedly allow its sustainable development as a whole. Taking into account the theory presented above with regard to the sustainable development of the real estate market and sustainable property value, the research carried out in this article will be able to provide decision-making support for real estate market players on both the demand and supply sides of the market.
It should be noted that the reports published to date in Poland concerning the residential real estate market are published in total, without a breakdown by the mentioned types of markets. This fact severely limits the utilitarian usefulness of reports prepared in this way, while the research carried out shows the need to create reports with a division between individual and developer markets. Taking into account the recommendations of the research will allow for sustainable development of the housing market.

2. Origins of Seasonal and Cyclical Fluctuations in the Residential Real Estate Market

Seasonal and cyclical fluctuations in the real estate market are important in terms of the pace of development of this market as well as being the cause of successes as well as failures of the entities operating in it. These fluctuations are a key factor in determining competitiveness due to their pervasive and dynamic impact on, among other things, real estate returns, risk, and the value of investments over time. Participants in this market, i.e., the demand side and the supply side, are placing increasing emphasis on the strategic and decision-making implications of the theory in analyzing periodic fluctuations in the real estate market [11]. The history of analysis in the area of periodic fluctuations in the real estate market originated in the 1930s when the concept of real estate land economics evolved into a discipline related to real estate investment [12]. One of the forerunners of the theoretical basis of the cyclical activity of the economy as a whole was Wesley Mitchel [13]. In a paper titled Business Cycles: The Problem and its Setting, he described economic cycles and their impact on price volatility, currency value, bank health, savings, investment, and speculation [14]. It is a position in the field of economics, with its foundations between Alfred Marshall’s Fundamentals of Economics (1890) and John Maynard Keynes’ General Economic Theory (1936). It is the first compact work on economic fluctuations to present the issues of cycles in a thorough and comprehensive manner, popularizing concepts such as oscillation, fluctuation, and rhythm that had long occupied the attention of leading economists of the time, such as Clément Juglar and Karl Marx [15]. Defining the business cycle as periodic changes in economic activity, exhibiting fluctuations over time in the volume of production, national income, investment, employment, and consumption, four phases can be identified in the cycle in classical terms, i.e., crisis, depreciation, recovery, and boom. In classical terms, the cycle is treated as medium-term periodic fluctuations. These cycles were identified in the American economy, as well as in the European economy, between 1825 and 1945. The theoretical basis of cyclicality for classical cycles is most extensively treated by neoclassical and Keynesian economics. The neoclassical approach is the assumption that fluctuations affecting the economy are exogenous and equilibrium is restored through internal mechanisms [16,17]. An alternative to the neoclassical school is the Keynesian theory, which explains cyclicality in the economy solely by internal conditions, mainly due to insufficient global demand [18]. After World War II, there was a spread of state-interventionist policies aimed at applying counter-cyclical and anti-crisis policies. This resulted in the transformation of cycles understood classically into modified cycles, in which two phases were distinguished, i.e., recession and economic boom. It is assumed that modified cycles occurred between 1949 and 2009. It should be noted that after the oil crisis and with the achievements of the technological revolution, there was a turn to new theoretical approaches from the early 1980s associated with monetarism [19].
The third evaluative approach to the theory of periodic fluctuations is the new classical school on the business cycle and the causes of crises [20,21,22,23] and the new Keynesian school [24,25,26,27,28,29,30].
The inclusion of periodic fluctuations over time should also be pointed out. A common division within the criterion of time allows us to distinguish four types of cycles and relate them to the real estate market. In particular, the user market, the market for development activities, the market for financial assets, and the land market [31,32]. We can divide cycles by time horizon into Kitchen, Juglar, Kuznetsov, and Kondratiev cycles [33,34,35,36]. It is assumed that Kitchen’s cycle, which lasts 4–5 years, is determined by the business cycle and, with regard to the real estate market, affects the user market, rents and prices in the market, as well as development activity. The Juglar cycle, unlike the Kitchin cycle, is the result of changes in supply and is estimated to last 9–10 years, affecting the development market. This cycle, due to its characteristics, causes the creation of oversupply. The third cycle is the Kuznets cycle, with a run length of 20–30 years. It is created by land prices. The longest cycles, with a run length of 45–60 years, are the Kondratiev cycles and are related to technological changes [31,33].
When summarizing the presented description of periodic fluctuations in the real estate market, it is important to point out its evaluative nature. The characteristics and patterns of cycles in the real estate market over time have adapted to the characteristics and patterns emerging in the economy. In the initial descriptions of cyclicality in the market, four of its phases were defined, with only two modifying over time. Moreover, the theory of cycles in terms of time allowed its identification in 4–5, 9–10, 20–30, or 45–60 year horizons.
In the real sphere, periodic fluctuations continue to be an issue addressed in scientific analysis, both in theoretical and practical terms. Current studies of cyclicality in the real estate market examining real estate prices over the long term have been described by Quigley [37]. His research focused on two aspects of real estate and the course of the real economy. In the first, he examined whether real estate market trends are predictable by fundamental factors in the economy, and in the second, whether exogenous trends in real estate prices—in fact, bubbles in the market—affect economic fundamentals. Interesting research in the area of real estate market cycles in the context of business cycles was also described by Żelazowski [38]. The purpose of his research was to identify similarities and differences in the formation of the housing market cycle and the business cycle. It is also important to point out the importance of research treating the aspect of the influence of economic or business variables on the cyclicality of the housing market. Research that identifies the influence of endogenous variables [39,40], credit constraints [41], purchase expectations [42], and financial intermediation [43] on cyclicality in the housing market allows for a more accurate understanding of the relationships occurring in the market. Additional useful studies introducing decision support in possibly determined real estate market forecasts are those in the field of identifying cyclicality and seasonality in the market [44,45,46,47,48,49].

3. Seasonal Fluctuations in the Housing Market—Identification of Annual Profiles

3.1. Purpose of the Study, Research Objectives, Research Implementation Algorithm

The purpose of identifying the so-called annual seasonality profiles is to study what fluctuations characterize the various stages of the housing construction process. In the process of parameterizing annual fluctuations, profiles were determined based on mean and median values. In the process of calculations and analyses, the formation of the number of building permits issued, the number of housing units whose construction has begun, and the number of new housing units put into use were considered. Each of the variables was analyzed in terms of total, individual investments, and investments made for sale or rent. The data for which the seasonality profiles were calculated came from the Central Statistical Office (CSO) databases, their interval is one month, and they cover the period 2005–2021. Data contained in CSO databases on the number of permits issued for the construction of apartments were obtained according to special forms from entities, i.e., county offices, cities with county rights, provincial offices, or districts of individual cities. On the other hand, the number of apartments put into use includes those confirmed by the contractor in the acceptance protocol. All data on the real estate market contained in the databases of the Central Statistical Office were obtained in accordance with the guidelines contained in the program of surveys of public statistics (in accordance with the relevant regulation of the Council of Ministers).
The algorithm of the conducted research is shown in Scheme 1.
The main purpose of identifying the so-called annual profiles is to precisely (quantify) the monthly values of the variables under study, especially their average values. Calculations were made for time series with and without a trend. The identification carried out is shown in two graphs. The first shows the profile for the mean and median for the studied variable with trend and the mean and median profile without trend. The second one calculates the average values of monthly fluctuations. Analyses were performed on data gathered from Poland.

3.2. Identification of Annual Profiles in Number of Constructions Permits Issued

The identification of annual profiles for the first stage of the housing construction process in total is shown in Figure 1. The figure includes annual profiles for the mean and median with and after eliminating the developmental (trend) component, which are marked with solid lines, whereas dots mark the actual data recorded in each year and month. Mean and median values were calculated for monthly data from 2005 to 2021.
In the determined profiles, in three months, i.e., January, February, and November, the average values of the observed fluctuations are below the average. The described regularity can be correlated with seasonal fluctuations and the resulting technical and technological capabilities of the residential real estate construction process. The annual profile obtained allows for the determination of the distribution of activity in total construction permits issued. The highest number of permits obtained, at 2140 units, took place in June, while the lowest value was in January (−3950 units).
In the next step, the identification of annual profiles was carried out for construction permits issued for investments made by individual investors. It should be noted that the nature of the identified monthly fluctuations in the calculated annual profiles is similar to fluctuations obtained for the number of total permits. However, the least active months are January, February, and November, and additionally, December, with July being the most active time (see Figure 2).
Figure 3 shows the identified annual profiles for permits issued for the construction of housing units for sale or rent. The results show that there is a decoupling from the seasons for developments that are predominantly large-scale projects.
In the case of individual investors, a correlation can be observed in the activity of building permits obtained with the seasons.
From the calculated annual profiles and from the realizations of monthly fluctuations derived from them, it can be concluded that over the year, there are three periods of increased activity in building permits issued for investments for sale or rent. The highest number of such permits was in October, December, and June, and the lowest activity was identified for January and November.

3.3. Identification of Annual Profiles in the Number of Housing Units Whose Construction Has Begun

In carrying out the identification and analysis of annual profiles for the second stage of the housing construction process, Figure 4, Figure 5 and Figure 6 were drawn up for total housing, housing whose construction was started by individual investors, and housing for sale or rent, respectively.
Overall, based on the determined annual seasonality profiles, for apartments whose construction has begun, it can be concluded that the highest activity occurs in February and March. It should also be noted that April has the highest number of apartments under construction. This number, after a slight decline, remains above zero in the profile until October.
It should be noted that in April, the number of total apartments under construction was 3500 units. For apartments built by individual builders, the characteristics of the identified profiles, and, thus, the monthly variability, are similar to the market as a whole (overall) (see Figure 5).
The identified annual profiles for dwellings for which construction was started for sale or rent are shown in Figure 6, from which, from an annual perspective, there are two cycles in the phenomenon under study. The first reaches its highest activity in April, while the second reaches its highest activity in October. Of the 12 months analyzed, positive values of the phenomenon under study were identified in as many as 8 of them, while negative values were identified for 4 months.
To economically interpret the formation of the identified profiles, it can be assumed that the values of low activity in this market in January, February, December, and July are due to seasonal fluctuations. It should be noted that for the winter months, the values of lower activity are due to the technological feasibility of construction. On the other hand, for August and September, it is possible to assume the hypothesis of a decrease in activity due to the holiday season.

3.4. Identification of Annual Profiles in the Number of Completed Housing Units

The last annual profiles described are those for housing units completed. The annual profiles for the average and total median are shown in Figure 7. Note the significant increase in activity in December. It should also be noted that five months, namely January, July, October, November, and December, show positive activity in terms of housing units added to use. The data show that the number of housing units added to use in December was 5140 units.
The calculated parameters of the annual profiles for dwellings added to use, realized by individual investors, are presented in Figure 8. It should be noted that the characteristics of the identified annual profiles are highly correlated with the characteristics of the profiles for new dwellings added to use in general.
The profile calculated for housing units completed for sale or rent is different from the two profiles described earlier. It is characterized by a trend of increasing activity since May (Figure 9). In contrast, from the annual profile, with the trend removed, only 5 months of the year are characterized by a positive value. These months, from the highest value, include December, October, November, July, and January, respectively. In December, market activity was calculated at 2120 units.
It should also be borne in mind that the average value achieved in December was affected by the abnormal situation in the real estate market at the end of 2008. At that time, there was an unnatural increase in the number of housing units completed due to legislative changes. These changes affect housing units in general, as well as those completed for sale or rent, and especially those developed by individual investors.

4. Statistical Analysis of Annual Profile Differences across Markets

In order to confirm the hypothesis that the annual profiles obtained from the 17 years for the 3 components of the housing construction process differ from market to market, i.e., in the total market, individual market, and developer market, a statistical analysis was carried out using the Z proportion test. The standard error of the difference between two proportions is calculated from the (1).
σ π 1 − π 2 = p ¯ ( 1 − p ¯ ) ( 1 n 1 + 1 n 2 )
p ¯ = n 1 p 1 + n 2 p 2 n 1 + n 2
where σ π 1 − π 2 —standard error of the difference between the proportions in the first and second trial, ( π 1 − π 2 ) —the expected difference between the proportions in the first and second trials, p 1 , p 2 —proportions in the first and second attempt, n 1 , n 2 —the size of the first and second attempts (17 years).
The null hypotheses have the following formulation:
H11 = 0.
The proportion of housing units for which building permits were issued in each month in the individual market is the same as in the development market.
H12 = 0.
The proportion of housing units for which a building permit was issued in each month in the individual market is the same as in the total market.
H13 = 0.
The proportion of housing units for which a building permit was issued in each month in the development market is the same as in the total market.
H21 = 0.
The proportion of housing units for which construction started in each month in the individual market is the same as in the development market.
H22 = 0.
The proportion of housing units whose construction started in each month in the individual market is the same as in the general market.
H23 = 0.
The proportion of housing units whose construction began in each month in the development market is the same as in the general market.
H31 = 0.
The proportion of housing units completed in each month in the individual market is the same as in the development market.
H32 = 0.
The proportion of housing units completed in each month in the individual market is the same as in the total market.
H33 = 0.
The proportion of housing units put into use in each month in the development market is the same as in the total market.
The critical value was calculated as (3). The significance level α was assumed at 90%.
( π 1 − π 2 ) + Z α σ π 1 − π 2
Z α is determined from the normal distribution table, and the exact probability of obtaining the difference between the proportions from the sample is calculated from (4).
Z = | ( p 1 − p 2 ) − ( π 1 − π 2 ) | σ π 1 − π 2
Before the calculations, the data were standardized, and then statistical significance was calculated in the following months across possible combinations.
Table 1 summarizes the obtained hypothesis values, with a value of 0 indicating that there is no variation in the data and 1 indicating that the variation is statistically significant.
The analysis shows the following:
  • For the individual and development market (H11), there are statistically significant differences in the proportions of housing units for which building permits were issued in May, July, October, and December.
  • For the individual and general market (H12), there are statistically significant differences in the proportions of housing units for which building permits were issued in October and December.
  • For the general and development market (H13), there are statistically significant differences in the proportions of housing units for which a building permit was issued in May, June, July, and December.
  • In the general and developer market (H21), there are statistically significant differences in the proportions of housing units for which construction began in May, July, October, November, and December.
  • For the general and developer market (H23), there are statistically significant differences in the proportions of housing units whose construction began in October, November, and December.
  • For the general and developer market (H31), there are statistically significant differences in the proportions of housing units completed in September, October, and November.
  • In other cases, statistically significant differences do not exist.

5. Summary

The seasonality profiles determined in the research process for all stages of the housing construction process allowed us to assess the nature of the periodic fluctuations that occur. The research carried out unequivocally showed that the individual and developer housing markets are characterized by different seasonal fluctuation profiles. The above was confirmed statistically using the Z ratio test. The different profile of seasonal fluctuations for housing built by individual investors in relation to housing built by developers makes it necessary to create different decision-making models for these markets. The seasonal profile for housing permits issued and for housing under construction for individual investors is characterized by an apparent seasonality that depends on the time of year. In contrast, for investors constructing apartments for sale or rent, there is no time-of-year-dependent seasonality. In the third stage of apartment construction, i.e., at the stage of putting the housing into use, apparent seasonality dependent on the seasons is absent. It should be noted once again that the reports published in Poland concerning the residential real estate market are published in total terms, with no division into the mentioned types of markets (i.e., the individual market and the market for sale or rent). This fact severely limits the utilitarian usefulness of reports prepared in this way, while the research carried out shows the need for reports with a division into individual and developer markets.
The research conducted was aimed at identifying seasonal fluctuations and indicating possible differences in seasonal profiles for the individual and real estate development markets without indicating the reasons for them. Taking into account the utilitarian aspect and economic theory, the causes of the identified differences should be sought in endogenous and exogenous factors, both market and technical—this very interesting aspect of the research will be carried out by the authors in the next stage of the analysis of the individual and developer markets.
The research results obtained and their integration into the economy will allow for the sustainable and balanced economic development of the region. Knowledge of the occurrence of seasonal fluctuations and their precise identification by the market of individual investors as well as those erecting apartments for sale and rent will allow planning and creating a sustainable and balanced development of this market for both public and local government institutions and entities representing the demand and supply side of the real estate market. It should be noted once again that, taking into account the criterion of market maturity and economic competitiveness, sustainable market development in the real estate sector is when it indicates its maturity through well-established values and transactions, and in terms of the region, confirmation that it has achieved a sufficient level of economic competitiveness and resilience to cope with economic cycles over time. The research carried out in the article, aimed at accurately determining seasonal profiles for both the market of development activities and those realized by individual investors, can certainly influence the maturity of the real estate market and, therefore, its sustainability.
Sustainability of the housing market, among other things, is knowledge of conditions in the demographic, social, economic, as well as financial systems, including knowledge of the different seasonality profiles of the markets included in it. Knowledge of the above will allow us to set parameters in the implementation of housing policy in Poland, both at the national level (e.g., provincial offices, cadastral departments) and the individual businesses on it (housing developers and buyers).

Author Contributions

Conceptualization, I.D. and P.F.; methodology, D.W., D.Z. and Ł.M.; validation, D.W.; formal analysis, I.D., P.F., D.W., D.Z. and Ł.M.; investigation, I.D., P.F., D.W., D.Z. and Ł.M.; resources, Ł.M.; data curation, D.W.; writing D.W. and Ł.M.; writing—review and editing, I.D. and P.F.; visualization, D.W.; supervision, I.D.; project administration, P.F. and D.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Buildings 13 00316 sch001 550
Scheme 1. Algorithm of the conducted research.
Scheme 1. Algorithm of the conducted research.
Buildings 13 00316 sch001
Buildings 13 00316 g001 550
Figure 1. Averaged annual profile. Permits issued for construction—total (Source: own compilation based on data from CSO).
Figure 1. Averaged annual profile. Permits issued for construction—total (Source: own compilation based on data from CSO).
Buildings 13 00316 g001
Buildings 13 00316 g002 550
Figure 2. Averaged annual profile. Permits issued for construction—individual (Source: own compilation based on data from CSO).
Figure 2. Averaged annual profile. Permits issued for construction—individual (Source: own compilation based on data from CSO).
Buildings 13 00316 g002
Buildings 13 00316 g003 550
Figure 3. Averaged annual profile. Permits issued for construction—sale or rental (Source: own compilation based on data from CSO).
Figure 3. Averaged annual profile. Permits issued for construction—sale or rental (Source: own compilation based on data from CSO).
Buildings 13 00316 g003
Buildings 13 00316 g004 550
Figure 4. Averaged annual profile. Housing units whose construction has begun—total (Source: own compilation based on data from CSO).
Figure 4. Averaged annual profile. Housing units whose construction has begun—total (Source: own compilation based on data from CSO).
Buildings 13 00316 g004
Buildings 13 00316 g005 550
Figure 5. Averaged annual profile. Housing units whose construction has begun—individual (Source: own compilation based on data from CSO).
Figure 5. Averaged annual profile. Housing units whose construction has begun—individual (Source: own compilation based on data from CSO).
Buildings 13 00316 g005
Buildings 13 00316 g006 550
Figure 6. Averaged annual profile. Housing units whose construction has begun—sale or rental (Source: own compilation based on data from CSO).
Figure 6. Averaged annual profile. Housing units whose construction has begun—sale or rental (Source: own compilation based on data from CSO).
Buildings 13 00316 g006
Buildings 13 00316 g007 550
Figure 7. Averaged annual profile. Housing units put into use–total (Source: own compilation based on data from CSO).
Figure 7. Averaged annual profile. Housing units put into use–total (Source: own compilation based on data from CSO).
Buildings 13 00316 g007
Buildings 13 00316 g008 550
Figure 8. Averaged annual profile. Housing units completed—individual (Source: own compilation based on data from CSO).
Figure 8. Averaged annual profile. Housing units completed—individual (Source: own compilation based on data from CSO).
Buildings 13 00316 g008
Buildings 13 00316 g009 550
Figure 9. Averaged annual profile. Housing units completed—for sale or rent (Source: own compilation based on data from CSO).
Figure 9. Averaged annual profile. Housing units completed—for sale or rent (Source: own compilation based on data from CSO).
Buildings 13 00316 g009
Table
Table 1. Summary of the results of the statistical analysis. (Source: own compilation).
Table 1. Summary of the results of the statistical analysis. (Source: own compilation).
JanFebMarAprMaiJunJulAugSepOctNovDec
H11000010100101
H12000000000101
H13000011100001
H21000010100111
H22000000000000
H23000000000111
H31000000001110
H32000000000000
H33000000000000
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MDPI and ACS Style

Frącz, P.; Dąbrowski, I.; Wotzka, D.; Zmarzły, D.; Mach, Ł. Identification of Differences in the Seasonality of the Developer and Individual Housing Market as a Basis for Its Sustainable Development. Buildings 2023, 13, 316. https://doi.org/10.3390/buildings13020316

AMA Style

Frącz P, Dąbrowski I, Wotzka D, Zmarzły D, Mach Ł. Identification of Differences in the Seasonality of the Developer and Individual Housing Market as a Basis for Its Sustainable Development. Buildings. 2023; 13(2):316. https://doi.org/10.3390/buildings13020316

Chicago/Turabian Style

Frącz, Paweł, Ireneusz Dąbrowski, Daria Wotzka, Dariusz Zmarzły, and Łukasz Mach. 2023. "Identification of Differences in the Seasonality of the Developer and Individual Housing Market as a Basis for Its Sustainable Development" Buildings 13, no. 2: 316. https://doi.org/10.3390/buildings13020316

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