As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

📌 Overview

"Living in India 2025" is a synthetic yet realistic dataset that explores the cost of living and quality of life across 200 Indian cities. It combines key indicators such as average rent, food cost, internet speed, healthcare rating, safety score, and happiness index to help analysts, students, and data enthusiasts perform in-depth comparisons and uncover meaningful insights. 📊 What’s Inside

The dataset contains 200 rows (one per city) and the following columns:

City – Name of the Indian city.

Average Rent (₹) – Estimated monthly rent for a standard apartment.

Food Cost (₹) – Average monthly food expenses per person.

Internet Speed (Mbps) – Typical broadband download speed.

Healthcare Rating (1-10) – Quality and accessibility of healthcare services.

Safety Score (1-10) – Perceived safety level in the city.

Happiness Index (1-10) – Overall life satisfaction rating.

💡 Potential Insights You Can Explore

Which Indian cities provide the best happiness for the least money?

How safety and happiness correlate across regions.

Which cities are most digital-nomad-friendly based on internet speed and cost.

Regional patterns in healthcare quality vs cost of living.

🛠 Ideal For

Exploratory Data Analysis (EDA)

Data Visualization Projects

Regression & Correlation Studies

Geospatial Mapping

Urban Economics & Policy Research

About This Dataset

This dataset is the original 70-city version used in my first published research paper: “A Data-Driven Survey on Cost of Living and Salary Affordability in Indian Cities” (IJRASET, 2025) Link: https://www.ijraset.com/best-journal/a-datadriven-survey-on-cost-of-livingsalary-affordability-in-indian-cities

It was created using web-scraping techniques from LivingCost.org and converted to INR using a consistent USD→INR exchange rate. This dataset forms the foundational base for affordability analysis, exploratory data analysis (EDA), and benchmarking cost-of-living patterns across India.

The dataset includes 70+ Indian cities, with fields covering living cost, rent, salary, affordability ratio (“months covered”), and derived financial indicators. It is clean, structured, and suitable for beginner to intermediate analytics projects.

Why This Dataset?

This dataset is ideal for:

EDA practice for college & school projects

Correlation and regression analysis

Basic ML tasks (predicting salary, affordability, rent, etc.)

Urban economics mini-projects

Dashboard creation (PowerBI, Tableau)

Data cleaning and preprocessing assignments

It is designed to be simple enough for students but structured enough for real-world analysis.

Features Included

Each row represents a city/state-level affordability profile with:

Cost of living (USD & INR)

Rent for a single person (USD & INR)

Monthly after-tax salary (USD & INR)

Income after rent

“Months Covered” affordability ratio

Source URLs for verification

Exchange rate used

This makes the dataset both transparent and reliable for academic usage.

Data Quality

Web-scraped directly from LivingCost.org

Cleaned and standardized

Currency converted uniformly

Non-city entries flagged

Fully reproducible from the source

This dataset served as the master input for my peer-reviewed paper and has been validated through statistical analysis.

Intended Audience

Students (school, undergraduate, postgraduate)

Data science beginners

Educators needing real datasets for teaching

Analysts looking for quick EDA practice

Researchers exploring affordability or urban economics

Note

A more comprehensive 200+ city enhanced dataset (used in my second paper) will be uploaded soon, including ICT metrics, GDP, and extended affordability indicators.

Dataset Description: Indian Urban Affordability and Economic Productivity (221 Cities) About the Dataset

This dataset represents the comprehensive 221-city version developed and utilized in the research paper “Predicting Urban Affordability and Economic Productivity in India: A Data-Driven KNN and Random Forest Framework with Insights from Selected Major Cities.”

It builds upon the author’s earlier 70-city affordability dataset and significantly expands its scope.

The dataset provides a unified framework to study how urban affordability, digital readiness, and GDP specialization jointly influence economic livability and productivity across different city tiers.

Data Provenance and Construction

Primary Source: Extended web-scraped affordability data originally compiled from LivingCost.org and other verified open-data platforms.

Cleaning & Standardization: City names normalized (e.g., “Bengaluru” → “Bangalore”), and all numeric fields standardized to INR using a consistent USD→INR conversion rate for comparability.

Features Included

Each record (row) corresponds to one city and contains the following metrics:

Cost of Living (INR)

Monthly Rent (INR)

Monthly After-Tax Salary (INR)

Income After Rent (INR)

Affordability Ratio (“Months Covered”)

Intended Applications

This dataset can be used for:

🧮 Cross-city affordability and livability analysis

🤖 Machine Learning model development (affordability or salary prediction)

🌆 Urban economics and policy simulation studies

📈 Correlation and regression-based research in ICT and GDP domains

📊 Dashboard and visualization projects (Power BI, Tableau, SAP SAC, etc.)

It is designed for use by researchers, policymakers, educators, and data analysts seeking a reliable, structured, and multi-domain dataset on Indian urban dynamics.

Data Quality and Transparency

✅ Uniform currency and value scaling

✅ Reproducible preprocessing (Python-based pipelines with Scikit-Learn)

✅ Missing values imputed using KNN-based methodology

✅ Verified against baseline datasets used in prior research

✅ Released under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Significance

This dataset forms the empirical backbone of the author’s second research paper, providing the quantitative base for the KNN baseline model and the Random Forest multi-output regressor used to predict salary and affordability across Indian cities.

It enables city-level insight generation for policymakers and supports reproducible, data-driven research in urban economics, digital inclusion, and sustainable development.

Future Extensions

An upcoming enhancement will include:

Complete AQI integration for all 221 cities to examine the affordability–environment linkage.

Time-series extension for multi-year trend analysis.

Inclusion of healthcare, safety, and green infrastructure indicators for a broader livability framework.

A additional file used in my paper on T30 cities of India with justification is also attached.

The average salary for male employees in urban area was the highest during the months of April to June 2020 at about ** thousand Indian rupees. The average salary drawn by female workers was the highest in the months of April to June 2020, however, lesser compared to their male counterparts. Unsurprisingly, the urban earnings in terms of wages and salaries are always higher than rural employees. Urban versus rural employment The gender gap in salaries was more prominent in rural areas, where, the male workers earned nearly an average of *** times more. However, urban employees just earn a few thousands more than their rural counterpart, while, the cost of living in cities is twice as expensive as villages. Moreover, a majority of the Indian households belonged to the middle-income bracket and this is expected to increase in the future. Wage disparity Wage inequalities are present in almost every sector and widens with higher skill levels. With the evident gender disparity in the country, women with lower educational qualifications, such as a high school diplomas continue working despite the pay gap. This is among women who primarily come from the lower economic sector. Moreover, the social mobility index for fair wage distribution was **** as of 2020, indicating a need for improvement.

According to a survey conducted in 2015 across India, over ** percent of the surveyed households had an average monthly income up to 10,000 Indian rupees. This percentage varied among the rural and urban areas, where over ** percent of the rural households and ** percent of the urban households earned up to 10,000 Indian rupees monthly. India had a high rate of rural to urban migration, as Indian cities provided better standards of living and employment opportunities.

Multiple income generators

For most of the population, income is earned in form of wages or salary, rent from residential or commercial property, interest from financial investments, and profits from family businesses. Most Indian households have multiple earning members to support consumption expenses on a day to day basis. During the surveyed year, around ** percent of the households had a single earner, mostly the head of the family, followed by about ** percent of households with two earning members.

Employment scenario

There are a lot of uncertainties in the job market in India. Non-availability of jobs matching education and skills was one of the main reasons for unemployment among Indian graduates. Underemployment was also a problem, and it was higher in urban areas than rural ones. Even though a majority of the population was self-employed, most jobs taken by workers had no written job contracts in both the salaried and casual employment sectors.

Urbanisation is a form of social transformation from traditional rural societies to modern, industrial and urban communities. It is long term continuous process. It is progressive concentration of population in urban unit. Kingsley Davies has explained urbanisation as process of switch from spread out pattern of human settlements to one of concentration in urban centers. Migration is the key process underlying growth of urbanization.

Challenges in urban development--->;

Institutional challenges

Urban Governance 74th amendment act has been implemented half-heartedly by the states, which has not fully empowered the Urban local bodies (ULBs). ULBs comprise of municipal corporations, municipalities and nagar panchayats, which are to be supported by state governments to manage the urban development. For this , ULBs need clear delegation of functions, financial resources and autonomy. At present urban governance needs improvement for urban development, which can be done by enhancing technology, administrative and managerial capacity of ULBs.

Planning Planning is mainly centralized and till now the state planning boards and commissions have not come out with any specific planning strategies an depend on Planning commission for it. This is expected to change in present government, as planning commission has been abolished and now focus is on empowering the states and strengthening the federal structure.

In fact for big cities the plans have become outdated and do not reflect the concern of urban local dwellers, this needs to be take care by Metropolitan planning committee as per provisions of 74th amendment act. Now the planning needs to be decentralized and participatory to accommodate the needs of the urban dwellers.

Also there is lack of human resource for undertaking planning on full scale. State planning departments and national planning institutions lack qualified planning professional. Need is to expand the scope of planners from physical to integrated planning- Land use, infrastructure, environmental sustainability, social inclusion, risk reduction, economic productivity and financial diversity.

Finances Major challenge is of revenue generation with the ULBs. This problem can be analyzed form two perspectives. First, the states have not given enough autonomy to ULBs to generate revenues and Second in some case the ULBs have failed to utilize even those tax and fee powers that they have been vested with.

There are two sources of municipal revenue i.e. municipal own revenue and assigned revenue. Municipal own revenue are generated by municipal own revenue through taxes and fee levied by them. Assigned revenues are those which are assigned to local governments by higher tier of government.

There is growing trend of declining ratio of own revenue. There is poor collection property taxes. Use of geographical information system to map all the properties in a city can have a huge impact on the assessment rate of properties that are not in tax net.

There is need to broaden the user charge fee for water supply, sewerage and garbage disposal. Since these are the goods which have a private characteristics and no public spill over, so charging user fee will be feasible and will improve the revenue of ULBs , along with periodic revision. Once the own revenue generating capacity of the cities will improve, they can easily get loans from the banks. At present due to lack of revenue generation capabilities, banks don’t give loan to ULBs for further development. For financing urban projects, Municipal bonds are also famous, which work on the concept of pooled financing.

Regulator

There is exponential increase in the real estate, encroaching the agricultural lands. Also the rates are very high, which are not affordable and other irregularities are also in practice. For this, we need regulator, which can make level playing field and will be instrumental for affordable housing and checking corrupt practices in Real estate sector.

Infrastructural challenges

Housing Housing provision for the growing urban population will be the biggest challenge before the government. The growing cost of houses comparison to the income of the urban middle class, has made it impossible for majority of lower income groups and are residing in congested accommodation and many of those are devoid of proper ventilation, lighting, water supply, sewage system, etc. For instance in Delhi, the current estimate is of a shortage of 5,00,000 dwelling units the coming decades. The United Nations Centre for Human Settlements (UNCHS) introduced the concept of “Housing Poverty” which includes “Individuals and households who lack safe, secure and healthy shelter, with basic infrastructure such as piped water and adequate provision for sanitation, drainage and the removal of hou...