Leveraging Affordable Business Innovations to Tackle House Price Rising: Applications of Machine Learning Algorithms

Abstract

Affordable housing as the most significant Sustainable Development Goal of the United Nations, is a burgeoning social problem across various nations globally. A lack of reasonable lodging to purchase or lease is fuelling the worldwide accommodation emergency. By 2025, 1.6 billion people are supposed to be unfavorably impacted by this. Starting from the beginning in the historical landmark, the housing market has grown quickly and land costs have been soaring. The land business has ended up being the support point industry of our general economy. Nonetheless, verifiable experience has repetitively demonstrated that, as financial advancement depends too significantly on land, these costs proceed to upsurge quickly there by outstripping demand over supply of land. Covid Pandemic is considered to have made the crisis much worse. The Indian housing market remains to skirmish with supply-side management for housing. Scarcity of technologically advanced and encumbrance-free urban land, the amplified cost of construction, absence of a feasible rental market and master-plan restraints have decreased the latent growth of the formal housing market in India.Taking cue from this, the research paper highlights the classic example of predicting the magnitude of Boston, USA and Indian Metropolitan cities housing prices by using regression techniques and Machine Learning Algorithms. The library packages used for this purpose are numpy, pandas, Scikit Learn and Seaborn. Housing prices are a basic picture of the economy, and its cost ranges are of remarkable interest to the purchasers and venders. Request a home consumer to define their dream house, and they possibly won’t initiate with the elevation of the basement ceiling or the nearness to an east-west railroad. In any case, this jungle gym rivalry’s informational index demonstrates more substantially cost of dealings.Keeping in view of the Contemporary Challenge of the housing crisis, the research paper forecasts the spiraling prices of houses dwellings and its association with various variables. The paper highlights accurate Regression Models and innovative Business Models in mainstream planning and development of cities.