How Data Pilot helped a federal ministry streamline its supply and demand of houses
Duration
9 Months - Nov’23 to Jul’24 till today
Industry
Government
Services
Advanced Analytics & BI, Data Engineering, Custom Data Solutions
Tools and Technologies
Company Background
The Ministry of Municipal, Rural Affairs, and Housing (MoMRAH) in Saudi Arabia is responsible for urban planning, infrastructure development, and housing initiatives across the Kingdom. It plays a key role in improving municipal services, developing affordable housing, and supporting long-term goals for sustainable development.
MoMRAH works to modernize cities, enhance living conditions, and implement smart city solutions, while creating opportunities for businesses in construction, real estate and public works.
Challenges
Couldn’t address demand and supply gaps
The client was facing difficulties in aligning house supply with demand for its customers, particularly in the context of the Saudi Vision 2030 initiative, which aims to increase home ownership rates among Saudi citizens.
No centralized view for data analysis
Because all their data was siloed from multiple, disparate sources, the client’s stakeholders were unable to assess the effectiveness of their government initiatives, such as loans and funding programs designed to support the system of allocating ownership to homes.
Solution
To solve these problems, Data Pilot helped build a solution that would provide actionable insights into current housing market conditions, forecast future trends, and assess the impact of government interventions. The solution would also enable the client to devise targeted and effective strategies for bridging the supply and demand gaps in the housing industry.
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Data Consolidation and Analysis
- Developing the Model
- Data Visualization Through Intuitive Dashboards
Our team started by analyzing all their data sources and figuring out their relationship with each other. During data consolidation, data points on the supply of houses were collected, such as the number of housing units available, their properties, and prices. Alongside this, applicant information such as age, affordability, and housing preferences was also consolidated.
An optimization model was built that would enable the client to leverage all their consolidated data and make key strategic decisions in the real estate industry. To build this model, Data Pilot used the PuLP module in Python to systematically address all challenges and optimize the distribution of housing resources.
Our solution was further supplemented with interactive dashboards that were designed to understand the current state of the housing market and predict future trends. The dashboards presented data on current bookings, contracts, available units, and targets across various regions, cities, and projects. This helped provide insights into how the supply and demand of houses fluctuate over time and how they vary across various geographic areas.
The model was equipped to carry out the following for the client:
Provide personalized discounts to applicants
The model included a discount allocation mechanism that would strike a balance between the discounts on housing provided to applicants and the budget they have available to provide these discounts. This mechanism helped reduce the financial burden on housing applicants in their buying process while ensuring the client does not incur any losses.
Future forecasts
Forecasting features were incorporated into the model’s dashboards to showcase future supply and demand trends based on historical data and predictive analytics. This helped ensure all strategic decisions were aligned with Saudi Vision 2030.
Optimized distribution of houses
The model was built to systematically address all challenges and optimize the distribution of housing resources.
It aimed to assign houses to applicants in a way that maximized the total number of houses assigned to applicants.
Automation and scheduling features
The model was scheduled to
run weekly using Windows Task Scheduler on an on-premises server. This ensured it would provide timely and up-to-date assignments for new applications and housing units.
The Impact
Increased operational efficiency
The automated model provided a systematic and efficient way to handle the dynamic nature of housing applications and unit availability.
Enhanced workflow in meeting housing demand with supply
The model enabled all stakeholders to make informed decisions for improving discount allocations and housing assignments, improving overall customer satisfaction and efficiency.
Industry Applications
Forecast Patient Needs in Healthcare
Hospitals and healthcare providers can optimize medical resource allocation and patient flow using predictive analytics. This enables providers to forecast patient needs and anticipate future changes in health, helping manage care and reducing operational bottlenecks, especially during public health crises.
Optimize Inventory Management in Retail
Retail chains can leverage this solution to consolidate data from various sources, such as customer purchases, regional trends, and online interactions, and use it to optimize inventory management across stores. Additionally, predictive analytics can identify patterns in customer behavior, allowing retailers to offer personalized discounts and promotions tailored to each region or individual, ultimately boosting customer satisfaction and loyalty.
Fleet Management in Logistics
Shipping companies can benefit from predictive models that help optimize delivery routes, fleet management, and warehouse distribution. By analyzing delivery times, traffic data, and geographic demand patterns, logistics firms can make real-time decisions that reduce delivery times and fuel consumption. This can also extend to predictive maintenance of vehicles, where the solution can help improve overall operational efficiency and reduce downtime.
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