A truly great rent forecast solution takes all known data from the current rent roll and applies appropriate assumptions to implement an expected value algorithm for each unit that can then aggregate up to any level of market or management hierarchy.

Here are examples of what great inputs should be:

Anyone with experience in rental housing knows that rents do not grow steadily throughout the year. There are clear high, low and shoulder seasons that must be modeled to create good forecasts.

No budget or forecast can be done without input defining how much rents are expected to grow over the period. Combined with rent seasonality, the model can then calculate market rents over the entire period of the budget or forecast.

Renewal retention rates

The percentage of expirations that are renewed. This drives the way the expected value (EV) model* assesses how much to apply new rents and vacancy loss vs how much to apply the expected renewal rents with no vacancy loss.

This assumption helps the model determine renewal rents. It's one thing to have new rent modeled as a single number by unit type for each month; however, that doesn't work well for renewals since renewal offers are typically based on a combination of both the market rent for a unit and the corresponding expiring rent for that resident. This can be easily expressed as a percentage of that difference or "gap." In budgeting and forecasting, be careful to express that parameter as the percentage of the gap that will be "achieved," not necessarily the percentage used to calculate the renewal offer. If the two are the same, then you are implying zero negotiations will happen. That may be desirable; however, it's rarely true.

This informs the EV model about the vacancy loss to calculate for the portion of the model that covers a home changing tenants.

Average LT is needed as part of the new lease branch of the EV model.

Needed to appropriately calculate gross vs net rents

Usually, this is based on an analysis of history with possible tweaks up or down depending on executive opinions on expected future improvement or degradation in residents' financial solvency.

Another assumption is needed to refine the model. As with bad debt, it is usually based on an analysis of history with possible tweaks up or down depending on executive opinions on future changes in market conditions.