1. Introduction
Housing is a basic necessity. It is recognized as a right in the Declaration of Human Rights[1] and in the International Covenant on Economic, Social and Cultural Rights, as well as in the constitutions of many of the world’s countries. However, in most countries there is a serious problem of access to housing for significant strata of society. Both in the acquisition and rental market, supply does not meet the demand. Over the last few decades, society has become more diverse, and with it, so have residential needs and preferences. Yet, supply has not kept up with the demand in this increased diversity and oftentimes remains the unchanged. The new social context calls for an adjustment in investment analysis methodology, introducing more information on demand requirements. This paper explores the changes that need to take place in the methodology so that the supply side is able to cater for diversity, i.e., that the analyses for supply analysis take into account the increased complexity by introducing into the market housing that provides enhanced social welfare. In addition, this paper explores the possibilities of big data and artificial intelligence as tools for the analysis of residential demand. Thus, the objective of the research is to advance in the definition of a methodology that is able to define a residential offer that meets a dual objective: to achieve a better adaptation to the needs of society while being able to provide greater profitability for the developer and/or investor. This research will be useful both for the supply agents from the private sector, developers and investors, and for the public sector in the field of housing policy and, more specifically, social housing.
The objective of advancing in a new methodology is twofold: to be able to define residential proposals more adapted to the demand and, at the same time, to generate greater profitability than that obtained with the traditional methodology. The proposal is based on the classic tools of feasibility analysis in the real estate sector, proposing the introduction of, on the one hand, demand variables that are capable of reflecting the diversity of housing needs, and on the other hand, the determination of sales prices taking into account the market and the real building costs associated with each dwelling.
2. Theoretical Framework
The world of real estate investment has undergone significant changes in recent decades. The global market and the entry of investment funds have significantly altered the process of analysis and decision making, assimilating it to a greater extent to that of the financial markets. One might contend that investments are more of a financial nature than a real estate investment. However, beyond the greater delocalization of capital, real estate is a traditional market, with a large local component, with very slow cycles that, on many occasions, do not respond to the demands of the financial markets. On the other hand, the consumer comes from an increasingly diverse society, and it is necessary to incorporate data that show this diversity through studies of people’s behavior and a more in-depth knowledge of the buyer’s needs.
There is relevant literature that identifies weaknesses in real estate analysis (Eldred & Zerbst, 1978; Graaskamp, 1980; Peterson, 1993). The major weaknesses to be mentioned comprehend errors in the estimation of relevant variables, the unavailability or impossibility of obtaining adequate amounts of quality data at the local level, and the indiscriminate use of aggregate data that are not appropriate for investments such as real estate, which have to take into account a local context, such as statistics on housing prices per square meter at the regional level and the use of national or regional economic information that is inappropriately applied to local analyses. Also noteworthy is the performance of flawed financial analyses due to a deficient estimation of relevant variables such as costs, revenues and expenses, as well as unrealistic and/or unfounded assessments. Shortcomings in real estate investment analysis and consequently flawed decision-making stem mainly from their complexity and subjectivity (Peterson, 1993).
Peterson (1993) points out four aspects that cause difficulty or deficiency in real estate analysis: complexity, conflicting aspect, qualitative aspect and difficulty of measurement. First, complexity derives from the existence of many different participants in the process belonging to different fields of knowledge such as geology, topography, architecture, interior design, construction engineering, finance, marketing and legislation. Second, real estate investment decisions may include aspects that entail conflicts both between different knowledge domains and within the same domain. For example, architectural design decisions may involve options that involve high investment costs and lower maintenance costs, or the opposite may be the case. The most viable solution will depend in part on the holding period expected by the investor. In addition, decision-making conflicts may arise, for example, in the case where the investor is also an end consumer. Third, real estate investment decisions may also have qualitative aspects or require value judgments. For example, in the case of finance, there are different instruments available whose best choice will depend on the creditworthiness of the investors and their risk preferences. Finally, fourth, investment characteristics can be difficult to identify and measure. In many cases, decisions involve multiple parties and interests and the relationships between them are often confidential or difficult to define. In other cases, the decision maker has to choose between balancing financial and aesthetic factors. Higher costs in the choice of aesthetics can improve the company’s image and sales and, in turn, raise construction costs.
Despite the fact that, as will be discussed in the following section, real estate investment projects do not sufficiently incorporate the preferences of the demanding parties, perhaps, as already indicated, the difficulty in obtaining information is an important limitation. However, there is extensive literature on the house purchase process. The demand for housing is multifunctional and hence depends on multiple factors, both those related to the characteristics of the dwelling itself and external factors related to accessibility, the quality of the environment or the socioeconomic level of the neighborhood (Al-Haddad, 2020; Bin Mohanna & Alqahtany, 2020; Chia et al., 2016; Hasanah & Yudhistira, 2018; Heriyati et al., 2021; Horne, 1991; Jalil et al., 2018; Kaynak et al., 2022; Lamsali et al., 2020; Li et al., 2020; Mang et al., 2020; Moghimi & Jusan, 2015; Mohd Thas Thaker & Chandra Sakaran, 2016; Nasar & Manoj, 2014; Rosser, 1999; Salah Al-Nahdi et al., 2015).
3. Context: Traditional Methodology in Investment Analysis
Economic-financial feasibility analyses of real estate projects are usually based on the determination of discounted cash flows based on a discount rate. While the calculations relating to the discounted values of the cash flows, NPV (Net Present Value) and the rate of return, IRR (Internal Rate of Return), are sophisticated, the simplicity of the reflection carried out for the estimation of the parameters on which these cash flows are determined is surprising. We even come across development companies that only execute very similar investments, always the same project replicated time after time, where the market is not studied and its suitability is not questioned. This conservatism contrasts with other markets where companies are constantly innovating and adapting.
Currently, potential locations are accessed through local commercial agents and a feasibility study is conducted based on three values: the cost of land, the estimated construction cost per square meter and the sales price per square meter in the area. Based on this information, a homogeneous product is defined in which the selling price of each unit responds mainly to the unit price established for the overall development.
Given the lack of data and the absence of a thorough analysis, the conclusions and the real estate products defined in the development tend to be simplified, opting for a standard product that is really adapted to a small market area. Thus, the feasibility of the project is determined on the basis of a limited and rigid real estate project that does not take into account the diversity of the demand.
The linear analysis of the cost of execution and the selling price, cost per square meter built and price per square meter sold, leads to the definition of a uniform product for which there is a considerable difference between the price/cost ratio between large and small units. In this situation, the differential between estimated revenues and costs will be minor for small dwellings, which are usually more expensive to build per square meter. As for larger homes, the homogenized price per square meter means that the total price will end up being very high and potential buyers of large houses will be forced to buy smaller ones. On the other hand, the institutions, lacking tools to facilitate customer management, are unable to offer personalized products. The result is a product that is not adapted to the client, an aspect that is also detrimental to the investor, as it prolongs sales times, generates surpluses and, in terms of opportunity cost, means a loss of potential income. Decision-making processes must take into account local culture, site-specific opportunities and the challenges of caring for the environment in addition to the preferences of the applicants. Ignoring this would lead to losing the innovative and socially conscious approach to architecture, Castellanos Garzon et al (2023).
4. An Increasingly Complex and Diverse Demand
Today’s society is witnessing increasing diversity in terms of demographics, lifestyles and housing preferences. This diversity is the result of a series of economic and social trends and changes that have emerged in recent decades. These changes have transformed, for parts of the population, the ways in which they live and work and, therefore, their housing needs. Demographic changes, such as an aging population and increasing ethnic diversity, have given rise to a more varied demand for housing that is able to adapt to new realities.
Demographic Changes
One of the key factors influencing the need for demand is demographic change. The aging of the population in many parts of the world is generating a growing need for housing adapted to the needs of the elderly, examples of which are senior living communities, assisted living and affordable housing. To cap it all, the low birth rate has considerably reduced the size of households, with a high number of households without children. Traditional housing, prepared for families with several children, is not adapted to the needs of the demand.
Immigration constitutes an additional catalyst for demographic transformation. The population tends to cluster in large cities with a greater capacity to generate employment, while other areas are left uninhabited. Migratory movements unbalance the relationship between supply and demand. While in large cities the supply does not meet the growing demand, in areas with a tendency to become uninhabited, the low demand discourages investment. These migratory movements also generate greater ethnic diversity, which is why a housing supply that reflects the cultural needs and preferences of different population groups is required.
Changes in Housing Preferences
Housing preferences have also evolved significantly. More and more people value sustainability and energy efficiency in their homes. In addition, labor mobility and a preference for living in urban areas have led to a demand for smaller, more functional homes, such as apartments and lofts.
There has also been an important change in the life priorities of young people who, in the past, placed family housing as a preferential objective. Now, however, there is a growing interest in flexibility and the possibility of change and adaptation to their specific situation at any given moment. In addition, the importance of community and social life is growing, which has led to a growing preference for mixed-use housing. In general, people are looking for more freedom of decision and mobility. This requires greater flexibility in terms of ownership, such as long-term rental and rent-to-own.
Social Change
The traditional family model has evolved significantly. Single-parent families, childless couples, singles, people sharing apartments with peers from the same peer group are more common. This requires a housing supply that accommodates diverse family structures, such as one-bedroom apartments and housing designed for cohabitation.
Also, users affected by personal needs or new professional trends such as working from home thanks to the past pandemic have generated new needs in the real estate market. Instances include residences featuring dedicated workspaces or environments conducive to improved health and safety.
5. The Need for Diversity in the Real Estate Product
Faced with this growing social diversity, it is essential that the real estate supply be adapted to take into account the existence of open and flexible ways of living, adapted to people at each stage or moment of their lives. Failure to do so can lead to an excess of supply for one type of family and to the dissatisfaction of a significant part of the population. Thus, three fundamental aspects are considered that should be taken into account in the supply in relation to the attention to diversity: typologies, forms of access and sustainability and quality of life.
The variety of typological options ensures that each individual or family can find the housing that best suits their needs and lifestyle. In addition, they must be designed to be adaptable to the changing needs of the residents.
It is also important to diversify the forms of access, acquisition is not the only option. Formulas that can adapt to different financial capacities should be considered. Long-term renting, renting with an option to buy, housing cooperatives, cohousing and coliving should gain ground over acquisition.
Housing should be designed with sustainability and residents’ health in mind. This implies the inclusion of eco-friendly features and the creation of spaces that promote well-being and quality of life.
6. The Potential of New Technologies for Demand Analysis: Big Data and Artificial Intelligence
The pervasiveness and ease of use of data in contemporary society is a fundamental element for its growth. Information technology introduces the possibility of automatically processing data and transforming it into information. In this historical and cultural moment, architecture has incorporated the digital transition. New tools have been introduced to facilitate and empower architecture, both in the process of conception and creation, Andaloro (2022).
To effectively address the diversity of the real estate market, it is crucial to use analytical tools that allow us to understand demand in real time and make decisions based on accurate data. Big data emerges as a powerful tool to achieve this. The innovation of information and communication technologies enables interaction and the ability, on the one hand, to personalise customer needs, and, on the other hand, to meet the needs of the producer/investor in order to obtain a return on investment.
Digital data becomes a tool for research and broadens the horizons of knowledge of the built environment, Taselli et al (2021). Big data in Real Estate involves the collection and analysis of large amounts of information, ranging from demographic and economic data to housing preferences and market trends. This information can be obtained from a variety of sources: public records, market surveys, social networks and home search applications.
Big data enables detailed analysis of demand in real time. By using advanced data analysis algorithms and techniques, search patterns, housing preferences and buying behaviors can be identified. This provides real estate investors with valuable information about which types of homes and locations are most in demand at any given time. Using different data collection techniques and the creation of a geo-referenced database, Pereira-Martínez et al (2022) describe a methodology for the analysis of location decisions. Specifically, they analyse the relationship between residential location decisions and economic/social parameters in university students.
On the other hand, one of the key benefits of big data is the ability to customize housing supply to meet the needs and preferences of prospective buyers or renters. This can include tailoring specific features of a home or creating customized packages to suit individual needs.
Artificial intelligence (AI) plays an essential role in defining a new strategy for the analysis and development of real estate investments. AI can analyze and process data in real time, enabling more informed and efficient decisions in the creation of real estate offers. It enables predictive analytics that help investors anticipate market trends. By identifying patterns in historical and current data, AI can forecast future demand and recommend appropriate investment strategies. Its key advantage is the ability to continuously learn. As society’s preferences and needs evolve, AI can automatically adjust investment strategies and housing supply to remain relevant and competitive in the market. AI is also useful for automating processes from project management and construction scheduling to property management.
7. Conclusion
The fundamental objective in defining a new strategy for real estate investments is to improve the product so that it is optimally adapted to the demand. This approach has two key objectives: first, to meet society’s changing needs and preferences, and second, to improve the profitability of the real estate market through greater efficiency.
Diversity in society is the result of a series of demographic and cultural changes. To meet these changing needs, real estate investors must be willing to adapt their strategies. This implies not only diversifying the supply of housing but also being flexible in terms of timelines and modalities of access to housing.
Globalization, among other factors, has brought about a business competition that makes an in-depth study of the consumer essential for any company. Unlike other markets, the real estate supply does not opt for a strategy of variety in the product, something considered essential by suppliers in other sectors where companies seek to offer the broadest possible variety so that the consumer can compare and decide within the models offered by the same company, avoiding, as much as possible, competition and guaranteeing the sale.
Diversifying and adapting real estate supply not only responds to demand but can also increase the efficiency and ultimately the profitability of the market. By focusing on what the population is really seeking and reducing oversupply in certain categories, investors can avoid unnecessary investments and improve their profit margins. Furthermore, it also reduces uncertainty about the ideal product, thereby significantly minimizing risks.
Firstly, the market must be studied to establish alternatives that meet the needs of the potential customers, sizes, equipment, location, among others, as well as to determine the prices that will optimize the profitability of the project. For these activities, new technologies such as big data and artificial intelligence are very useful. At present, AI tools such as artificial neural networks and machine learning are quite common in the estimation of real estate asset prices.
Secondly, once the relevant information has been identified, the assumptions or alternatives in the definition of the real estate assets that make up the project will be established. The real estate investment analysis tool should be able to determine the effects on profitability by introducing different typologies and prices. As it should assist in decision making, the tool should provide information on the effects on profitability of altering the initial typology definition. In other words, how different alternatives in terms of size distribution and prices will affect profitability in each case. The tool should move away from linear approaches.
Thirdly, the real estate investment analysis methodology is inherited from the investment analysis of other types of assets such as financial assets, where a single period is assumed for the duration of the investment. The new methodology must be able to establish different periods and modes of return of capital in the same project. It must be able to include scenarios where, in the same project, homes are sold under various alternatives: payment at the time of key entry, rental, rental with option to purchase, among others. Addressing diversity will mean a more complex analysis, but as a result, the investment will be able to define a real estate project that is more adapted to the needs of the consumers and with a higher profitability.
In sum, the research should advance in three aspects: 1. the identification of relevant variables in the study of demand, 2. the introduction of AI tools for information processing and estimation of demand needs, 3. the introduction of diversity in the calculations of basic investment analysis tools such as NPV and IRR.