Harnessing Predictive Analytics for Success in the Student Housing Industry

In today's fast-paced world, leveraging data for strategic decision-making is crucial for staying ahead of the curve. The possibilities of data-driven operations are endless. The dynamic student housing market is also subjected to these observable patterns. The data patterns can come in very handy if organised & measured in a specific manner with predictive analytics.

Historical data on enrollments, student demographics, regional demand & supply, average rental prices, etc., can be refined to predict future trends and events. This blog explores the role and impact of predictive analysis in the student housing industry.

What is Predictive Analytics?

Predictive analytics is the ability to anticipate the probability of a specific phenomenon based on statistical algorithms, machine learning techniques, and source data. In the predictive model, raw data is first processed into structured data, and then patterns are identified to predict future events.

In student accommodations, rental contracts depend on the college/university semesters. The renting period varies anywhere from 6 months to 12 months. The key indicators of student accommodation demand are steady occupancy levels and prospective interest generated by students from nearby universities.

It is integral to understand the correlation between occupancy levels and factors like rental trends, enrollment trends, and location-specific occupancy trends. However, predictive models can provide accurate forecasts by incorporating these variables.

These models delve into details such as location, seasonality, average rent, and enrollment projections. This foresight equips operators to not only prepare for new student cohorts but also optimise bed supply to maximise seasonal profits.

Due to seasonal demand and contracts, property operators need to evaluate current performance metrics and conduct a thorough competitor analysis. Moreover, they need to forecast demand and profitability before peak booking seasons.

Predictive Analytics Types

As per Butterflymx, the predictive analytics types constitute 4 major steps and can be applied to student housing as well.

Description: What is the Market Situation?

The initial phase involves examining past and current events. In the student housing sector, this entails gaining insight into various aspects such as occupancy rates, enrollment trends, sources of demand, rental growth, operational expenses, and historical profits spanning previous years and recent periods. This data is a valuable resource for analysts to discern recurring trends over various seasons.

Diagnosis: Mapping the Reasons Why?

The second step involves unravelling the underlying reasons for the observed phenomena identified in the "Description" phase. In essence, it seeks to understand the driving forces behind the patterns derived from the data collected. During this stage, a series of questions may emerge, including:

1. "What is causing a decline in occupancy rates in student housing at location X?"
2. "What is behind the sudden surge in demand during a specific month in location Y?"
3. "What factors have contributed to a Z% increase in operational costs for my student property over the last six years?"

While addressing these inquiries, predictive models consider historical data to pinpoint the causative elements responsible for the observed patterns. This phase highlights the growing prominence of correlations between factors such as enrollment figures, geographical location, and inflation rates concerning occupancy levels, demand, and operational expenses.

Prediction: What May Happen?

The prediction stage is the culmination of gathering a comprehensive dataset and uncovering the causal factors. In the context of the student accommodation sector, which is known for its resilience but still subject to uncertainty, the reliance on historical data to explain past events and their causes is vital.

The future is subjected to numerous variables; hence, the prediction stage requires a lot of current statistical data. This includes recent enrolment figures in local institutes, the prevailing average rent for student lets in the area, and the operational costs incurred in the present month. With the help of these insights, the predictive model can estimate occupancy levels for the upcoming season/semester, rental income, operational costs for the season, revenue, and profits.

Prescribe: What Needs to Be Done?

A robust predictive analytics model goes beyond offering projections and estimates; it also provides actionable prescriptions based on its predictions. Within the student housing sector, prescriptions contain strategies aimed at optimising occupancy rates, implementing dynamic pricing strategies, identifying cost-cutting measures, and formulating standards to maximise ROI, among other recommendations. This stage of the predictive analytics model leans towards planning and increasing the value of the student property.

Techniques of Demand Forecasting

Forecasting demand in student accommodation can be achieved mainly with two approaches, the Trend Projection Method and Regression analysis. The trend projection works on the premise that past trends will continue. Whereas Regression Analysis constitutes dependable and independent variables and assumes a linear relationship among them. It can be explained with an equation:  Y = a + bX.

Variables in Regression Analysis for Student Housing Property

A. Dependent Variable: The dependent variable in this analysis is what one is trying to predict or explain. In the case of student housing rental property, this could be:

1. Rental price: If the prediction is for rental prices.
2. Occupancy rate: If the prediction is for the percentage of units that will be rented out.

B. Independent Variables: These are the factors or variables that influence the dependent variable. For student housing rental property, independent variables might include:

1. Location: The proximity to the educational institution or university can significantly affect rental prices and occupancy rates.
2. Enrollment figures: The number of students enrolled in nearby universities or colleges can impact the demand for student accommodation.
3. Amenities: The presence of amenities like furnished units, Wi-Fi, security, and laundry facilities can influence rental prices and demand.
4. Local economic conditions: Factors like local job market conditions, which can affect student part-time employment opportunities, may also play a role.
5. Competing housing options: The availability of other housing options, such as on-campus housing or nearby off-campus accommodations, can affect demand.

How amber Utilises Predictive Analytics in Student Housing?

Predictive analytics requires various types of data sets related to the student accommodation property and the market. Amber understands its importance and and has it’s own practices in place such as:

A. Multidimensional Insights

Amber helps its partners with detailed dashboards of its performance compared to market trends. Partners can derive multi-dimensional insights, demand trajectory, and market preferences to help assign a price to their products and create bundles of amenities and services.

B. Conversion Analysis

The amber Connect dashboard allows partners to track their listings' performance by showing the number of enquiries received, the number of bookings in process, and the number of completed bookings. These data points can be used to map out the conversion metrics for the partner portfolio. Additionally, partners can track the revenue generated from processed bookings and the average commission percentage for all bookings.

1. Enquiries: This is the number of students who have expressed interest in the partner's listings. This could include people who have sent a message to know more about the property.
2. Bookings Requests: Partners get to know the number of booking requests for their properties have received. This section indicated the number of interested students who clicked the “Book Now” button and raised their requests.
3. Bookings Processed: This section shows the number of bookings that have been fully completed and the student’s contract has been confirmed.

The conversion metrics for the partner portfolio can be calculated by dividing the number of completed bookings by the number of enquiries. This will give the partner a percentage that shows how many enquiries they are converting into bookings in a given period. Furthermore, the partners can track the revenue generated from all the completed bookings.The dashboard also shows a customisable timeline graph that sets fundamentals for our partners to observe the trend and predict the number of bookings they can expect in the near future.C. Market Share AnalysisMarket share analysis is a great way for our partners to track their performance and identify areas where they can improve. The amber Connect market share analysis provides partners with a macro-to-micro perspective of their business, including:

1. Destination and Source Countries: Our partners can check from which source countries students are booking properties. With this data, they can monitor the global interest in their portfolio.
2. Booking share: Partners can also track the percentage of bookings that their properties have acquired. This is an identifier of the competitiveness of their properties.

By tracking these metrics, partners can identify areas where they can improve their performance. For example, if a partner has a low pageview share, they may need to improve their listing descriptions or photos. If a partner has a low lead share, they may need to improve their marketing campaigns, and in case of a low booking share, they may need to make their listings more competitive.With amber Connect’s analytics, property management groups and operators can form a foundation for their very own predictive model and derive actionable insights to achieve higher profits. For a more detailed orientation, list with amber today and take advantage of an exclusive amber Connect dashboard.Pathway to SuccessWhile the demand for student accommodation is expected to rise in tandem with these enrollment increases, operators and management groups must adapt to evolving consumer expectations to maximise profits from their properties.The dynamic global student accommodation market can rely on predictive analytics to decipher patterns from historical data, guiding operators in setting optimal rental prices and occupancy goals. Predictive analytics, a tool proven to boost profits across various industries, is a valuable asset that the student housing industry should embrace to stay competitive and capitalise on this growing demand.