Introduction

Land is considered an indispensable natural asset that holds paramount significance for all facets of developmental undertakings, particularly in urban settings. The issue of land scarcity pervades the densely woven urban fabric of the global landscape. The situation becomes worse when available land remains underutilized for assorted reasons, giving rise to what we term “urban voids.” These voids are found in diverse forms such as abandoned structures, vacant parcels, deteriorating infrastructure, residual spaces amidst buildings, and areas beneath elevated roadways (R.O.B.), among others. Urban voids represent unoccupied or underexploited areas within a city that lack a defined purpose and are often perceived as neglected due to numerous factors. Ensuring the judicious use of this finite resource is a paramount concern for all stakeholders. Inadequate land utilization can potentially lead to catastrophic consequences within urban areas. The land possesses multifaceted attributes, encompassing physical, social, economic, and environmental dimensions while the economic aspect plays a pivotal role in contributing to the overall advancement of urban areas [1].

An extensive study of the literature suggests the significance of land valuation for a range of stakeholders, including local governments, brokers, valuers, planners, money lenders, bankers, and politicians [2]. Developed nations have established robust data sources to accurately assess land valuations. Consequently, the real estate sector in developed countries has made substantial contributions to the state’s economy through transaction charges and property taxes [2]. However, numerous studies have acknowledged the challenges associated with real estate valuation in developing nations. These challenges become evident from the scarcity and unreliability of data, as mentioned by Armiah’s 1992 study on sub-Saharan Africa [3] and Sharma and Newman’s 2018 research on the land value of Bangalore, India [4]. In developing countries, income generated from property assets lags that of developed countries due to the unavailability and unreliability of transaction data [5]. The most important aspect is that India’s real estate market distinguishes itself from both developed nations and other developing counterparts by virtue of its unique social and economic backdrop. The value of urban land in Indian cities has witnessed exponential growth, propelled by commercial opportunities, and accompanying amenities [6]. Notably, the migration to urban areas in India significantly influences housing density and property prices [5]. Major cities that have been found with substantial migrant influx, such as Mumbai and Delhi, have witnessed a remarkable upsurge in property prices over the past few decades [5]. On the other hand, various forms of urban voids persist, adversely impacting overall development. These urban voids, particularly in developing countries, where resource constraints and the neglect of certain areas are prevalent, can become the potential with numerous benefits [7].

Recognizing the potential contributions of these vacant spaces to a city’s social, economic, and environmental dimensions is crucial for effective urban planning and development. It is important to note that there is a shortage of published research on real estate-related aspects within the Indian context, due to the lack of relevant data availability.

Research problem

Most studies conducted in the Indian subcontinent have focused on a macro-level context [5]. To gain a comprehensive understanding of the spatial distribution and factors influencing land values within urban voids in a specific geographic region, there is a need for studies conducted at the micro level. Also, no studies have been identified that specifically address the critical zones within the western part of Indian cities. In accordance with various urban planning policies, urban areas should ideally develop in a harmonious and uniform manner.

This study aims to bridge these gaps by identifying various urban voids, gathering data on their land valuations, and analyzing the factors that influence land values. The findings from this research can contribute to the identification of critical zones within Jaipur city, potentially leading to the refinement of urban policies [8, 9]. This study not only addresses the research gap but also expands the scope of land valuation studies in a data-deficient market like India by conducting a micro-level land valuation study of urban voids in a tier-2 Indian city.

Research objective

The objective of this research is to identify key factors influencing land valuation in the context of urban voids within Jaipur city, Rajasthan. This endeavor aims to provide valuable insights for policymakers, development authorities, and various stakeholders, enabling them to conduct a detailed analysis at the micro-level of urban voids and their potential for sustainable development. Such insights can inform the refinement of policies, urban planning strategies, and financial mechanisms, including transaction charges and property taxes within the area. By pinpointing critical zones, these entities can formulate infrastructure proposals to each zone, fostering a more uniform and sustainable development approach. Furthermore, this research sets the stage for future investigations, offering a framework for utilizing urban voids in alignment with the aspirations of the local population. It facilitates the development of planning and financial frameworks to advance Sustainable Development Goals.

This research serves as a base work for identifying varied factors that impact land valuation, with potential applicability in different regions worldwide. In contrast to the traditional comparable method commonly used for land valuation in Jaipur, this study employs advanced modeling techniques that leverage transaction data and land attributes to establish statistical models. This approach is expected to yield a deeper understanding of the property market dynamics [10]. The study’s methodology involves the development of a model to analyze urban land values in Jaipur, the capital city of Rajasthan, India. This model integrates property values with socioeconomic data, survey information, and geospatial data using Geographic Information Systems (GIS). The resultant model will highlight specific characteristics of the property market in urban Rajasthan.

Research methodology

Figure 1 shows the research methodology.

Fig. 1
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Research methodology (ref: authors)

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Literature review

The review is divided into three parts: Part one briefly summarizes various approaches used in land valuation. Part two summarizes the factors affecting land valuation. Finally, part three describes land valuation methods used in various research papers.

Approaches and issues

The land valuation approaches and issues have been identified by various authors in different timelines and in different countries and regions. Different authors have identified various approaches and issues for land valuations such as the land valuation data in developing countries are not reliable to get strong results so other techniques such as E-portal and survey need to be part of data collection [3, 4]; other papers suggest that detailed research related to land value is missing in Indian subcontinents. It should be based on geographical location, so it is required to see land valuation in a micro context [5, 11]; there is a comparison of the land market between developing and developed nations and socio-economic parameters play a key role in defining the land market in developing nations [6]. Other research paper suggests that the land value increment cannot be checked in India because reliable data are not available and translation of property is quite different in developing nations and the actual value cannot be determined by the registered value in the revenue department, so survey data is more reliable than published data [5, 12]. There is a research gap related to land valuation of vacant land [13]. Other researchers suggest that the published data related to land valuation is very little because reliable data is not available, so it is needed to find other sources for land value data collection [14]. From the above studies, it is clearly stated that land valuation is a complex matter and still it has not been worked upon judiciously as well as land valuation data cannot be taken from published data.

Factors affecting land values

The study is required to see land valuation in a micro context so that numerous factors can be identified and relationships can be established. Authors of different timelines and regions have identified numerous factors that can affect property values. These factors are regional-specific, but they give us clues to critically evaluate through quantifiable techniques.

Some of the factors suggested by authors such as location and accessibility factors affect land valuation [15]. Infrastructure facilities are the determining factor for land valuation [16]. Zoning and height regulation determine the land price [17]. Transportation costs play a key role in land valuation [18]. Property attributes, geographic location, and facilities play a key role in land valuation [19]. Proximity to CBD and transportation network play a key role in land valuation [20]. Accessibility to transit developments and land location is the main cause of the rise in land value [21, 22]. Socio-economic and neighborhood characteristics affect land prices [17]. Accessibility, physical attributes, regulations, and socio-economic conditions affect land prices [23]. Numerous factors have been identified in different timelines and regions. Some of the factors have loosened up due to fast-changing technological interventions, changes in the socio-economic fabric, and other factors. These factors can be utilized as variables that may affect the land valuation of the area.

Land valuation methods

Various research papers suggest that land valuation methods have been used at different times and in different parts of the world; for instance, one of the research papers suggests that the traditional method can be used for land valuation [24] (Fig. 2). Different hedonic approaches are used as statistical models to calculate land prices [25]. Advanced modeling methods are now adopted in land valuation techniques [26]. The study suggests that O.L.S. is used to calculate land value in different geographical locations [19]. Spatial error model has been used to calculate land prices [27]. Remote sensing and GIS should be used for data generation and preparation of models [28]. From these studies, it is evident that land valuation is a complex task; it requires a strong relationship between different variables. Advanced land valuation methods such as the spatial regression model, ordinary least square methods, and hedonic price models have been used to identify numerous factors involved in ascertaining land value. These quantifiable methods can be used in different study areas. Advanced land valuation method has been utilized in Thiruvananthapuram, Kerala, a southern part of India city—3rd Tier city [14]. A similar method can be used in other parts of Indian cities. These variables have been tested in the western part of India and other research papers can target different regions of the eastern and northern parts of India.

Fig. 2
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Land valuation process (ref. authors)

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Study area

Jaipur has been chosen as the study area and divided into various wards. It is well connected with all metro cities. It is a tier-1 city with a population of 4,107,000 (about twice the population of New Mexico) (about twice the population of New Mexico) population of which 3,471,847 (52.40%) live in the urban region of the district (Census 2022). Investment in properties is considered a very lucrative option for investments. Jaipur residential colonies were set up by housing societies in the initial stages, these colonies were not well planned, so we find lots of deficiencies in terms of facilities, width of roads, schools, and other infrastructure facilities. On one side, we find lots of deficiencies and on the other side lots of urban voids are visible due to assorted reasons; this type of leapfrog development is not sustainable development. This research paper is an attempt to find urban voids and check the economic potential of chunks of land in different parts of the cities. The Jaipur Master Plan 2025 indicates that approximately 50% of the city is designated for residential use. Other zones, including commercial, institutional, and recreational areas, are distributed across various parts of Jaipur. Various industrial sectors are in different areas throughout the city. The CBD area, established in 1727, represents the walled city of Jaipur. Various multi-nodal hubs of commercial establishments are evident across various parts of the city. The predominant commercial land use aligns linearly along major highways and roads, as outlined in the Jaipur Master Plan 2025. Different industrial setups have been established in various parts of the city in different timelines. Labor housing has been established around industrial setup. Migration inflow can be seen, and it has affected the housing density in various parts of Jaipur city. Socio-economic variations are also visible. Different communities such as Hindu, Muslim, Sikh, and Christian communities are living with different occupations and economic standards. Lots of labor colonies are visible around the industrial area. Some of the restricted zones such as Military areas, UNESCO-protected areas, and ecological sensitivity areas also existed. The walled city of Jaipur has been designated as a UNESCO-protected city (Fig. 3).

Fig. 3
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Study area map (ref: mapdata@2024)

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Methods

Aim

The aim of the research paper is to investigate the viability of numerous factors/parameters that can affect urban void land valuation. Additionally, the study will determine whether these factors significantly contribute to any specific piece of land or if their contributions vary across different cities, regions, or countries.

Design and setting of study

Data collection

Numerous factors identified through the literature review affect land value for Jaipur city (Fig. 4). Different questionnaires have been floated to different stakeholders associated with real estate, construction, and planning in different government and private organizations (Fig. 5). Various experts’ and officials’ opinions were taken through Google form.

Fig. 4
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Land valuation methods

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Fig. 5
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Selection of stakeholders for Survey data (ref: authors)

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Around 48 responses were received from government and private organizations and the results are shown in Figs. 6, 7, 8, 9, and 10.

Fig. 6
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Accessibility factor affecting land value (ref: authors)

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Fig. 7
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Proximity factor affecting land value (ref: authors)

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Fig. 8
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Environmental factor affecting land value (ref: authors)

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Fig. 9
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Socio-economic factor affecting land value (ref: authors)

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Fig. 10
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Plot attributes factor affecting land value (ref: authors)

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Expert opinion suggests that factors such as the distance to the national highway/state highway, major district roads, and proximity to rail terminals (considered accessibility factors); proximity to the central business district (CBD), major employment hubs, the quantity of commercial buildings, and proximity to schools and hospitals (proximity factors); closeness to garden areas, slum areas, landfills, and industrial zones (environmental factors); proximity to specific communities and housing density (socio-economic factors); and availability of water supply, land in planned development, and land use (plot attributes) significantly influence land value, followed by the distance to lower roads and bus terminals (accessibility parameters), proximity to nallah and proximity to cremation area (Environmental factor), proximity to lower income area (socio-economic factors), and plot in a restricted zone and availability of sewage network (plot attributes). Other factors have a comparatively lesser effect on land value.

Data survey

One hundred urban void locations were identified and marked on a map through a physical survey (Fig. 11), and data were collected for land value and other explanatory variables. Euclidean distance was used to calculate distances [18, 29,30,31]

Fig. 11
figure 11

Urban void location on Jaipur master plan with ward boundaries (ref: authors) (source: Google Earth Pro and GeoDa software)

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Jaipur city does not have reliable land record data. The primary reason is that registry charges are extremely high and the DLC rate from which land transaction occurs is exceptionally low as compared to actual land transaction. Therefore, land value data for this study was collected from e-advertisements through portal 99acres (about half the total floor space of the Pentagon) from Jan 2021 to Feb 2023. 99acres (about half the total floor space of the Pentagon) is the most used online property search portal in Jaipur city, so its data has been collected. Several studies have used advertisement prices due to the non-availability of data and have reported significant results [32,33,34,35,36]. The final 100 locations were selected, and a physical survey was done to check the viability of online data through discussion with property agents.

The higher values were seen in major landmarks such as Jawahar Circle and central park and on the edge of major roads.

The mean value of land in Jaipur is 70,161 rupees per square meter, with values ranging from 8000 rupees to 200,000 rupees (Table 1).

Table 1 Variables details [12]
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The study utilized 100 variables as represented in Table 1. The data for these variables were generated through Google Earth Pro map and field survey. The explanatory variables were classified into five categories: accessibility, proximity, environmental, socio-economic, and plot attribute variables [14].

Accessibility parameters

Seven variables under this category were chosen through literature review and expert opinion. Variables were measured as Euclidean distance. The variables in this category represent various proximity measures such as distance to highways, MDR, lower road, bus terminal, rail terminal, airport, and bus stop (Table 1) [37].

Proximity parameters

Eight variables under this category were chosen through literature review and expert opinion. Except for the no. of commercial buildings within 1-km distance, all other variables were measured as Euclidean distance. The variables in this category represent various proximity measures such as distance to CBD, peripheral towns, hospitals, schools, tourist places, employment sources, recreation area, and no. of commercial buildings (Table 1).

Environmental parameters

Seven variables under this category were chosen through literature review and expert opinion. Variables were measured as Euclidean distance. The variables in this category represent proximities to industries, cremation, nallah, railway line, landfill, slum area, and garden area. Expect proximity to a garden area and other proximities have adversely affected land value (Table 1).

Socio-economic parameters

Three variables represent proximity to a specific religious/cast community in the study area. Jaipur has specific communities in various parts of the city, and it has been added through expert interventions. Housing quality has been taken as a key detriment in comparison to housing density. Proximity to low-income groups has also been taken as there are lots of labor housing near industrial areas (Table 1).

Plot attribute parameters

Nine variables have been used. The permissible FAR value, distance to regulated zones, permissible height, approach road width, planned development, plot size, available water and sewage network, and land use are the variables from this category. Jaipur lies in Zone-2 of the disaster map of India, so it is comparatively safe as well as it is a dry city, so disaster and water logging variable have not been considered [38, 39].

Identification of statistical method

An ordinary least square (OLS) regression model has been used to test the land valuation through statistical methods. Urban voids have been marked in various locations on the Jaipur map and all the points have been geo-referenced Google Earth Pro and Euclidian distance has been marked to get the actual distance of different parameters to the point (Urban void) [37]. Standard error, adjusted R2, and VIF factors have been analyzed through JMP and SPSS model [40,41,42].

Model methodology

Figure 12 shows the modeling methodology.

Fig. 12
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Modeling methodology [14, 43, 44, ]

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A total of 100 samples of urban voids have been taken in various parts of Jaipur city. To check the multicollinearity effects, variance impact factor (VIF) has been analyzed through SPSS and JMP models (Table 2). The VIF has been found below 10 so multicollinearity impact has not been found in the data set. There is no sharp dip between R2 and adjusted R2 in the OLS model (1%); also, Prob>|t| is 0.406 so the model is quite significant to use and analyze (Table 3) [45,46,47].

Table 2 OLS regression coefficients through SPSS model
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Table 3 OLS model statistics through SPSS model
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Now to further check that each factor is important in determining land values in Jaipur city, a further analysis of the variables was done through the application of principal component analysis and rotation loading that are equal or greater than 0.50 are considered high and acceptable.

The MSA value for proximity to bus stops, bus terminals, and middle-income groups has been found below 0.5 so that factor cannot be taken for analysis (Fig. 13). The Proximity to industries, availability of water supply and sewage network, and permissible building height has negative coefficient so it is not possible, so this factor be removed and distance from higher income group has positive impact that is also not practical so this factor has been removed to remove all these factors, we can get following results

Fig. 13
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MSA analysis (JMP Software model)

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The VIF has been found below 10 so multicollinearity impact has not been found in the data set (Table 4). There is no sharp dip between R2 and adjusted R2 in the OLS model (1%); also, Prob>|t| is 0.767 so the model is quite significant to use and analyze (Table 5).

Table 4 Upgraded OLS regression coefficients through SPSS model
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Table 5 Upgraded OLS model statistics through SPSS model
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Multiple regression analysis: This was undertaken to determine the significant contributions of the 24 factors out of 32 in influencing land values in Jaipur city (Fig. 14).

Fig. 14
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MSA analysis (JMP Software model)

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Y = B0 + B1f1 + B2f2 + B3f3 + … + B24f24 +E

Y = value of land as rated by 24 factors.

E = error

The estimated coefficients and their p-values are presented in Table 6—contribution of factors to land values.

Table 6 Factor’s role in affecting land value through SPSS model
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Based on their p-values, varied factors have been ranked. By the ranking, factor 6 contributed more followed by factors 21, 20, 23, 11, 02, 17, 22, 08, and 14 in that order. Other factors have also contributed based on their rankings.

The result of the multiple regression analysis provides the measure for ranking the 24 factors influencing land values in the study areas based on the magnitude of their contribution.

Results and discussion

The stakeholder survey data (Fig. 5) underscores the considerable impact of various independent variables on the land value of urban voids. These variables were assessed using statistical tools across 100 surveyed urban voids in different parts of the study area, revealing the following comparisons or analyses.

Among accessibility factors, such as distance to the national highway/state highway, major district roads, and proximity to rail terminals, statistical analysis of the surveyed data reveals that the distance to major district roads holds a significant impact on land value. However, other factors do not contribute as significantly.

Among proximity factors, such as proximity to the central business district (CBD), major employment hubs, the quantity of commercial buildings, and proximity to schools and hospitals, statistical analysis of the surveyed data reveals that the number of commercial buildings in that area as well as proximity to major employment sources (offices) holds a significant impact on land value. However, other factors do not contribute as significantly.

Among environmental factors, such as closeness to garden areas, slum areas, landfills, and industrial zones, statistical analysis of the surveyed data reveals that the proximity to nallah, proximity to landfill area, and proximity to cremation/burial grounds hold a significant impact on land value. However, other factors do not contribute as significantly.

Among socio-economic factors, such as proximity to specific communities, statistical analysis of the surveyed data reveals that proximity to specific communities, proximity to low-income area, and proximity to restricted zone hold a significant impact on land value. However, other factors do not contribute as significantly.

Among plot attributes, such as availability of sewage and water supply, statistical analysis of the surveyed data reveals that available road width and plot in planned development hold a significant impact on land value. However, other factors do not contribute as significantly.

The expert opinion serves as a foundational framework for analyzing land values; however, subsequent studies indicate that evaluating land value necessitates a more granular, micro-level approach.

The study primarily focuses on checking the importance of the context and its impacts on land value. The infrastructure or other proposals that may affect land value have not been considered. Various other advanced methods such as spatial regression and residual methods can be used in further research. This research establishes a strong relationship between different independent variables and land values.

Conclusions

This investigation focused on identifying independent variables that can influence the land value of Jaipur city. The researcher used expert opinions to determine these variables and then tested them using an ordinary least squares (OLS) model to assess their significance. The data collection process involved both online portals and physical surveys to obtain land values of different urban voids in various parts of Jaipur. The locations of these voids were mapped using QGIS with longitudinal and latitude coordinates. Subsequently, the researchers used JMP and SPSS to calculate R2 (coefficient of determination) and VIF (variance inflation factor) factors, which are common statistical metrics in regression analysis to understand the relationship between variables.

The study concluded that expert opinions can be a valuable starting point for identifying relevant independent variables, but it is crucial to validate them through various statistical methods like regression analysis (OLS) to ensure their significance. It was also noted that different cities may have their unique attributes, and the impact of independent variables on land value can vary significantly from one city to another.

The investigation underscores the significance of comprehending the local context and employing suitable statistical methodologies to discern the determinants influencing land value in specific cities like Jaipur. This study holds the potential to establish diverse toolkits for calculating land value based on the locational attributes of urban voids, allowing administrators and policymakers to judiciously assess and apply charges for sustainable development. These identified factors serve as a guide for urban planners and land managers, ensuring optimal utilization of land. Furthermore, this research can be integrated with other attributes such as zoning regulations, land titles, irrevocable power of attorney, and more, enhancing the robustness of the findings.