Without exaggeration, the concept of housing, home, or any place representing a family’s residence carries a profound and sacred meaning across all societies. Outside the home, humans engage in a constant and prolonged struggle for survival – facing threats from cold, heat, wind, and rain. It is only within the home, among loved ones, that a person truly feels at peace [2].

For many families worldwide, housing represents the largest expense and the most valuable asset, while also being the most crucial factor determining Quality of Life (QoL). Although in many developed and capitalist economies, the number of housing units meets demand, a significant portion of the global population still lacks access to adequate and financially attainable housing. According to a report from UN-Habitat, nearly one billion urban residents worldwide live in slums [3].

During the second United Nations conference on human settlements (1996) in Istanbul, adequate housing was defined as follows: “adequate shelter is not merely about having a roof over one’s head; it entails appropriate comfort, sufficient space, accessibility, physical security, ownership security, structural durability, proper lighting, ventilation, and heating systems. It also includes essential infrastructure such as water supply, sanitation, education, waste management, environmental quality, health standards, and a location with access to employment and basic services – all of which must be provided in an affordable manner” [4].

However, in Iran, housing holds significance beyond its fundamental purpose as a shelter. It serves additional functions and provides financial benefits. The type of housing (rental or ownership), the neighborhood, the property’s size, and its associated factors contribute to material investment for individuals and can transform into cultural capital – a concept described by Bourdieu [5] as symbolic capital. Symbolic capital has the potential to convert into other forms of capital throughout a person’s life, thus playing a pivotal role in shaping individuals’ lives [6].

Housing refers to one of the critical issues in developing countries. The lack of sufficient resources, weak economic management, the absence of comprehensive housing planning, and other deficiencies in the economic infrastructure of these countries, combined with the rapid growth of the urban population, have made the provision of shelter a complex and multidimensional challenge. Meeting the increasing demand for housing not only requires land, capital, construction materials, and a large workforce, but also necessitates a planning system to ensure coordination between the housing sector and other sectors, as well as between the essential components of housing – land, construction materials, and infrastructure, including public utilities, social services, and transportation systems [2].

Today, housing is a primary factor in social integration, a major commodity, and a decisive element in society, playing a significant role in shaping individual identity, social relationships, and collective goals [7].

It can be argued that the housing issue exists worldwide. However, in developing countries, due to rapid population growth and urbanization, internal migration, insufficient financial resources, land supply challenges, difficulties in securing construction materials, and a shortage of skilled labor – along with the absence of appropriate policies, strategies, and plans for land and housing – the problem has become severe and critical. The rapid pace of industrialization has driven rural populations toward cities. In many cases, the social aspect of industrialization does not translate into affordable housing, social services, or improved worker welfare. Consequently, the number of people living in slums and impoverished urban areas increases daily [2].

In these countries, budget allocation to the housing sector is generally lower than other economic sectors. There is little recognition of the crucial fact that investment in housing is an investment in the development of social and human resources, with a high potential for job creation. According to United Nations estimates, to address housing problems in developing countries, ten residential units per thousand people must be constructed annually. However, in reality, the number rarely exceeds two to three units per thousand people [8].

Based on this, the current study focuses on evaluating the efficiency of the 22 districts of Tehran, conducting a quantitative comparison of housing market developments in the Spring of 2023 and 2024 using the Data Envelopment Analysis (DEA) approach. It seeks to answer the question: how have housing market developments in Tehran evolved during this period?

The structure of this paper is as follows: initially, the output-oriented CCR model is introduced, followed by input-free radial models in DEA, and then the input-free CCR model. The Andersen and Petersen’s ranking model [1] is subsequently explained, followed by the AP-CCR input-free ranking model. Next, the selection of input and output variables is presented, followed by the research findings. The paper concludes with the

Plebankiewicz [9] examined the main criteria for contractor evaluation. Tsenkova [10], in a study employing comparative analysis, investigated changes in housing efficiency during the transition from government control to private sector management of the housing market. She argued that the challenges in achieving efficiency in the housing sector could be examined from multiple dimensions, including technical, social, and financial aspects. She concluded that reducing government intervention and transferring housing market management to the private sector would enhance efficiency in the sector.

Gilbert [11] analyzed a Colombian government project aimed at constructing housing for 100,000 impoverished families. His study explored the rationale behind adopting public housing policies and assessed opportunities for the successful implementation of this project. Gilbert [11] questioned the effectiveness of government-led housing initiatives in addressing homelessness and noted that previous similar projects had failed to resolve the issue.

Sepehr Doust [12] presented a paper titled “a comparative study of housing production efficiency in the country”. The objective of this study was to assess the technical efficiency of the housing construction sector in Iran and to determine the status and future outlook of this industry in the country. DEA and an input-oriented CCR model with the assumption of constant returns to scale were used to evaluate the technical efficiency of the housing sector and conduct a relative comparison at the provincial level during the period 2006 to 2009. Moreover, the supplementary BCC model, assuming variable returns to scale, was employed to decompose technical efficiency into managerial efficiency and scale efficiency. The findings indicated that the average efficiency of provinces in housing construction was 0.94, and relatively, only 37% of the provinces had technical efficiency in housing production. On the other hand, approximately 63% of the provinces were inefficient, with most of them exhibiting decreasing returns to scale. It was also found that other inefficient provinces suffered from managerial inefficiencies related to the suboptimal use of production inputs.

Vafadar Asghari et al. [13] published a paper titled “evaluating relative efficiency of Mehr housing projects via DEA technique (the case of cities of Sistan and Balouchestan province with population of more than 25000)”. This study measured the relative efficiency of cities with populations exceeding 25,000 in Sistan and Baluchestan in executing Mehr housing projects from early 2008 to late 2011 using the output-oriented BCC model within the DEA framework. The input variables used in this study included human resources, physical resources, and financial resources. The number of constructed housing units and the employment generated by this project were considered as output variables. The findings showed that the Mehr housing projects were efficient in the cities of Chabahar, Khash, Zahedan, and Konarak, while they were inefficient in Iranshahr, Zabol, and Saravan.

Jafari et al. [14] identified opportunities and threats within the mass housing industry and formulated effective response strategies for each. The results indicated that the opportunities for mass housing development in Mazandaran province included an increase in the maximum amount of government-provided housing loans, an increase in the number of government-provided housing loans, a reduction in loan interest rates, a high rate of return on investment in the housing sector compared to other economic sectors, rising inflation in the housing market, increasing migration to cities, the adoption of new construction technologies, and the decline of substitute products such as single-unit houses.

Addressing housing at the societal level is one of the primary objectives of Iran’s national programs. This is evident in the country’s constitution, which, while emphasizing the comprehensive well-being of individuals, recognizes housing as one of the fundamental needs and obligates the government to mobilize all its resources, facilities, and capacities to provide housing. One of the key aspects of housing services management in Iran is the provision of housing loan services to various segments of society. This is further highlighted by the special attention given to housing in the country’s development plans. Given that one of the most practical techniques for evaluating housing sector efficiency is DEA, this study employs this powerful mathematical approach to analyze the developments in Tehran’s housing market transactions during the Spring of 2023 and 2024.

DEA consists of a set of techniques used to analyze production, cost, revenue, and profit data without parameterization and technological indexing. DEA is a non-parametric mathematical programming approach for estimating frontier functions. In general, the foundation of non-parametric methods for efficiency measurement was established in 1957 with the publication of a paper by Farrell [15]. Using a purely mathematical approach, he introduced a novel method for measuring modern efficiency as an alternative to parametric methods. Farrell [15] was the first to define the “Farrell efficiency frontier” as a non-parametric boundary. Based on mathematical methods and the distance of firms from the desired frontier, he was able to measure the efficiency frontier. Farrell’s theories laid the groundwork for subsequent research in this field. Farrell’s theories attracted not only mathematicians but also management scholars and economists, who began exploring non-parametric structures for efficiency measurement instead of production functions and parametric methods.

Despite these efforts, the significance of this approach remained largely unnoticed for two decades after Farrell’s influential paper [15] until, exactly 21 years later, in 1978, the paper by Charnes et al. [16] was published. These researchers introduced DEA for the first time, which subsequently led to extensive studies expanding and applying DEA methodology.

Charnes et al. [16] initially proposed a model with an input-oriented nature, assuming “constant returns to scale”. Later studies introduced more flexible assumptions, like the BCC model in 1984, which proposed a “variable returns to scale model” [17].

Today, DEA is recognized as a suitable method for evaluating firm efficiency. In this method, based on available data, the efficiency frontier is empirically estimated. Since all data points contribute to defining the frontier, the method is referred to as “DEA”.

More than 47 years after the introduction of the first DEA model, numerous models have been developed in this field, and its practical applications have expanded to thousands of studies [18], [19].

As stated in the findings, the evaluation indicators for Tehran’s 22 districts were extracted through a review of previous research and literature studies. One of the key considerations in real estate investment is recognizing that decision-making in this market follows a multi-variable process. Various factors, including demographic structure, government economic policies, the price of housing sector inputs, business cycles affecting the housing sector, housing tax regulations, land ownership laws, urban planning projects, and investment conditions in other economic sectors, all influence real estate investment decisions.

The volatile nature of the housing sector on both the supply and demand sides, coupled with the numerous internal and external factors affecting it, has created a profitable environment for economic players. While investors seek to capitalize on market fluctuations, individual homebuyers purchase housing primarily for personal use and family residence.

According to Table 4, buyers in Districts 19 and 22, after ensuring that purchasing a home meets the minimum economic and investment justifications, proceed with their purchase, focusing primarily on the well-being it provides for their families. In this demand-driven process, the motivation for investment strengthens when the return on investment surpasses the general inflation rate or the risk-free investment return rate (such as bank deposits). This segment of demand is mainly shaped by risk-averse households, seek to preserve their wealth and assets in the long term, and, given their long-term perspective on homeownership, do not consider investment liquidity a major concern.

This type of investment-oriented demand materializes within the real estate asset market. According to Table 4, districts 10, 5, and 3 recorded the highest transaction volumes, driven mainly by investors who purchase residential units to benefit from capital gains resulting from housing price appreciation. It must be acknowledged that what distinguishes housing market developments from those in other sectors and leads to unique price fluctuation patterns is primarily the influence of investment-oriented housing demand. In such conditions, districts 5 and 10 registered the highest transaction volumes. Economic entities that specialize in and focus on real estate investment strategically plan their future investment activities by analyzing market history, monitoring current indicators, and considering key market-driving factors to maximize market opportunities while minimizing risks.

Evidence suggests that, in general, policymakers are dissatisfied with speculative housing demand. The primary reasons for this dissatisfaction include the complexity of evaluation processes and shortcomings in a comprehensive assessment system. As mentioned previously, subjective methods alone cannot fully reflect market realities, and a more objective approach must be considered when evaluating the 22 districts of Tehran. Various objective methods exist for ranking market efficiency, with DEA being one of the most advanced and effective techniques. Given that DEA is one of the most practical tools for efficiency assessment, the present study applied this powerful mathematical technique to calculate the housing transaction volume across Tehran’s 22 districts.

It is recommended that inefficient districts in Tehran’s housing market transformations take their reference districts as benchmarks for structuring, utilizing, and managing input resources. Moreover, attention should be given to other significant housing market indicators, and future research should incorporate these factors to yield findings more aligned with housing market realities. Furthermore, the application of hybrid DEA models integrated with multi-criteria decision-making techniques such as AHP, BWM, and SWARA is suggested for more precise weighting of indicators. Finally, the adoption of more advanced models, such as interval-based or fuzzy models, is recommended for achieving more accurate results.

The document does not explicitly mention any funding sources.

The study used raw data from the National Real Estate and Housing Transactions Registration system, published by the Central Bank’s Economic Research and Policies Department.

No conflicts of interest are mentioned in the document.