Harnessing Predictive Analytics for Success in the Student Housing Industry

The student accommodation market experiences tactile shifts with every passing academic year. While some events like pandemics and war are uncontrollable and remain unforeseen, the student accommodation market, despite its dynamic nature, is still subjected to observable patterns. These patterns can be observed through the historical data on higher education enrollments, student demographics, regional demand and supply, average rental prices per unit in the region, etc. Accumulating the data sets based on such patterns gives you the greatest power that all businesses aspire to be empowered with, and that power is predictive analytics. This blog explores the role and impact of predictive analysis in the student housing market.

1. Introduction to Predictive Analytics and Its Relevance in Student Housing

• 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 are dependent on the college/university semesters; the renting period varies anywhere from 6 months to 12 months. In the UK and Australia PBSAs usually follow the weekly contract, where a particular price is set per week for the entire duration of the contract. The crucial indicators of demand for student accommodation are the sustained occupancy levels and the level of interest it generates among students from educational institutions for the season in its vicinity.
• While the correlation between occupancy levels and factors like rental trends, enrollment trends, and location-specific occupancy trends may not be readily apparent through simple observation, predictive models can provide accurate forecasts by incorporating these variables and delving into location, seasonality, average rent, and enrollment projections. This foresight equips operators to not only prepare for new student cohorts but also optimize bed supply to maximize seasonal profits.
• Owing to seasonal demand and contracts for student accommodation operators, it’s critical not only to consider their current key performing metric and compare it to the industry at large but also to be able to forecast the demand and profitability ahead of the season.

2. Data Attributes For The Student Accommodation Properties

The operational predictive analytics model requires various types of data sets related to the student accommodation property and the market. Taking a reference from the Alexsoft blog following infographic consists of groups of data attributes to be considered while building your own model for your property.

3. Predictive Analytics Model in Student Housing

As per Butterflymx, the predictive analytics model in the real estate sector constitutes 4 major steps. The following sections describe these steps in relevance to the student housing industry.

1. Description: (What is happening/What happened?)

The initial phase involves the examination of past and current events. In the student housing sector context, 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.

2. Diagnosis: (Why it is happening?/Why it happened?)

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.

3. 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. However, the future is subjected to numerous variables, hence the prediction stage requires a lot of current statistical data. Which includes recent enrolment figures in local institutes, 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 cost for the season, Revenue, and Profits.

4. Prescribe (What should 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, these prescriptions should contain strategies aimed at optimizing occupancy rates for the season, implementing dynamic pricing strategies, identifying cost-cutting measures, and formulating standards to maximize return on investment (ROI), among other actionable recommendations. This stage of the predictive analytics model leans towards planning, giving suggestions that lead towards building and growing the value of your student accommodation property.

4. 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 you are trying to predict or explain. In the case of student housing rental property, this could be:

Rental Price: If you want to predict rental prices.

Occupancy Rate: If you want to predict 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:

Location: The proximity to the educational institution or university can significantly affect rental prices and occupancy rates.‍

Enrollment Figures: The number of students enrolled in nearby universities or colleges can impact the demand for student accommodation.

Amenities: The presence of amenities like furnished units, Wi-Fi, security, and laundry facilities can influence rental prices and demand.

Local Economic Conditions: Factors like local job market conditions, which can affect student part-time employment opportunities, may also play a role.

Competing Housing Options: The availability of other housing options, such as on-campus housing or nearby off-campus accommodations, can affect demand.

5. Dynamic Price Setting

• Setting rent for a student housing property is primarily dictated by property type, room size, room type, number of rooms, amenities, operational costs, distance from nearby universities or colleges, average rents in the area, current demand and occupancy rates in the area. The key to garnering maximum rental yield lies in striking the right balance between rent setting and expected occupancy levels.
• Once a thorough demand forecast is established, operators can make informed pricing decisions. This involves considering the current supply of local student accommodation, setting prices strategically to meet revenue and yield targets, and achieving the desired occupancy levels for the upcoming season.
• Finding the right balance between rent setting and occupancy goals is pivotal. Prices can be adjusted based on demand projections and market conditions. Operators may choose to set rents higher during peak demand periods or offer discounts and incentives during slower seasons to attract tenants. The ultimate aim is to optimize rental income while ensuring that rental rates are attractive to prospective students, thereby, achieving a profitable and well-occupied student accommodation property. This is where amber’s student housing solution suite helps our partners optimize revenue with our strategic data-driven solutions on amber Connect.

6. Amber Connect: The Comprehensive Analytical Suite

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.

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 customizable 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.

Market Share Analysis

Market 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 us today and take advantage of an exclusive amber Connect dashboard for your student housing portfolio.

7. Conclusion

Reputable research studies are foreseeing substantial growth in global higher education enrollments, particularly with increasing international student intakes. This trend is triggering spontaneous transformations in the student accommodation landscape, particularly in countries like the UK, the US, Australia, and Canada. While 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 maximize 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 capitalize on this growing demand.