Note from Tami: Still true today! Grow Taller 4 Idiots
By Brad Marting, CPM, CAPS 2000
It is often said that establishing maximum rental rates can determine the success or failure of a multifamily housing investment. But it is more often said than done.
Historically, rents have been increased based upon overall occupancy percentage. This is reasonable because occupancy is an indication of supply and demand. However, waiting for ideal occupancy has made decision makers hesitate in raising rents. The time lost during the decision-making process creates missed opportunities.
While market forces, the economy, competition, vacancies, and concessions often have provided an excuse to hold back on raising rents, none of these factors has as much of a negative effect upon rental rates as does fear – the fear of raising rents.
The following situation is quite common: The financial reports for January are received February 15th, and occupancy was 94 percent. That’s a little low, and we don’t know if rents have truly stabilized so we wait another month. The financial reports for February show occupancy was 94 percent again. That’s very good, but we had probably better wait another month. The financial reports for March reveal that occupancy fell to 92 percent. Better not raise rent now, we’ll wait another month and see.
The financial reports for April are received May 15th, and occupancy increased to 95 percent. Now things are looking good, but we’ll wait another month just to e certain it was not a fluke. The financial reports for May show occupancy was 98 percent. It is felt rents have stabilized at a sufficiently high occupancy percentage, and rents are increased. Unfortunately, five-and-a-half months have passed without an increase, and only 2 percent of the units are now available to receive the rental increase (98-percent occupied).
No wonder rental income is growing very slowly. As seen by this scenario, when determining rental rates, reaction time is critical, and time is money.
The Marting Solution
During my more than 20 years in property management, including several years in New York renting out New York sublets (which we all know is a pretty competitive real estate market,) I have sought an objective tool which would assist in determining the maximum rental rates. The system needed to be simple and easily administered, so I developed one myself.
I have been using and improving the system for more than three years at different properties across the country. Rental increases upon lease renewal of $50 to $125 per unit, per month have been common. The system is very simple, easy to use, and is based upon the law of supply and demand. It takes less than 30 minutes per week to do. I call it the Marting Rent Matrix System (OK, I’m not the most creative person).
It is necessary to make a couple of simple assumptions in order to go in Golden Valley to use the Marting Rent Matrix System. First, it is assumed rents are market driven (supply and demand). Second, it is assumed that for each percentage of occupancy, there is a corresponding level of acceptable rental rate. Note that any property can achieve any occupancy level of rate adjustments are unrestricted. If $1 were charged for monthly rent, the property would certainly be 100 percent occupied. If $10,000 were charged for monthly rent, the property would likely be 100 percent vacant. The question remains as to what rental rate will maximize the income to the property for what desired level of occupancy? Experimenting with the Marting Rent Matrix System will help find that point of maximized rents.
Figure 1: Marting Rent Matrix
Unit Status | Total Units | % of Total | A (600 S.F.)** | B (750 S.F.) | C (850 S.F.) | D (1100 S.F.) |
Units Not Leased | ||||||
Vacant-Not Leased | 4 | 3% | 2 3% | 1 3% | 1 3% | 0 0% |
Notices-Not Leased | 5 | 3% | 1 2% | 0 0% | 3 8% | 1 5% |
Potential Vacancy | 9 | 6% | 3 5% | 1 3% | 4 10%* | 1 5% |
Vacant-Leased | 3 | 2% | 1 2% | 1 3% | 1 3% | 0 0% |
Notices-Leased | 5 | 3% | 2 3% | 0 0% | 2 5% | 1 5% |
Leased but not moved in | 8 | 5% | 3 5% | 1 3% | 3 8% | 1 5% |
Occupied | 145 | 91% | 53 88% | 37 93% | 37 93% | 18 90% |
Executive Suites | 6 | 4% | 3 5% | 0 0% | 1 3% | 2 10% |
Model Apartments | 1 | 1% | 0 0% | 1 3% | 0 0% | 0 0% |
Employee Apartments | 1 | 1% | 1 2% | 0 0% | 0 0% | 0 0% |
Total Occupied | 153 | 96%* | 57 95% | 38 96% | 38 95% | 20 100% |
Total Leased | 156 | 98% | 58 97% | 39 98% | 39 98% | 20 100% |
Sum of Units | 160 | 100% | 60 | 40 | 40 | 20 |
Total Units | 160 | 60 | 40 | 40 | 20 |
* Percentages have been rounded up for illustration purposes and may not appear to add up.
** More data on unit types can be found in Figure 1A below.
Figure 1A: Marting Rent Matrix
Unit Description Legend
Last This Week’s Week’s Sq. Ft. Sq. Ft. Code Type Rents Rents Per Mo. Per Year A 1/1 $575 $575 $0.96 $11.50 B 2/1 $625 $628 $0.64 $10.05 C 2/2 $680 $679 $8.80 $9.59 D 3/2 $810 $813 $0.74 $8.87 |
Total Weekly $ IndexCode $ Change # of Units Totals A $0 3 $0.00 B $3 1 $3.00 C ($1) 4 ($4.00) D $3 1 $3.00 Weekly Grand Total $2.00 |
How it Works
Basically, the system evaluates the rent and occupancy levels for each subgroup of units on a weekly basis (remember, reaction time is critical). The initial setup of the system is very important:
- Break the unit mix into smaller, homogenous groups that share common characteristics, such as one-bedroom upstairs units, two-bedroom one-bath downstairs units, etc.
- Use 20 to 60 units per group.
- Use six to nine groups.
- Count all units – include models, employee units, guest suites, etc.
- Use base rents – you can add premiums later.
- Set a benchmark occupancy percentage – the overall occupancy percentage you desire. (Note: 100 percent occupancy is not necessarily a good thing; it merely shows that you are not maximizing rents, and it’s time for an increase.)
Realize and accept there will be exceptions for longer-vacant, hard-to-rent units. These units receive a special concession which does not affect the rest of that particular group (usually units more than 60 days vacant).
Once the Marting Rent Matrix System is set up as a Lotus or Excel spreadsheet, as in Figure 1, simple enter the number of vacant, occupied with notice, occupied, vacant but rented, etc. The system does all the math and shows the occupancy percentages instantly. Each group, “A” through “Z”, is evaluated based upon the potential vacancy percentage (number of vacant units plus the number of occupied nits with notice to vacate within 60 days).
If the ”A” group’s potential vacancy is 4 percent, and your benchmark is 95 percent occupancy, you would increase the rent for that group slightly, say by $2. If the “A” group’s potential vacancy is 9 percent, and your benchmark is 95 percent occupancy, you would decrease the rent for that group slightly, say by $3. Because the system includes the units occupied with notice to vacate within 60 days, you have this period of time to adjust the rents to the maximum level without incurring vacancy losses on those units. (Residents are still paying rent until they vacate).
How much to raise or lower rents will vary by property and must be determined by experimentation. The rents will seek their own levels of equilibrium, and rents will be maximized. If you raise rents too much, occupancy will increase, and you will lower the rents the following week to the maximum level the market will bear.
One advantage experienced with adjusting rents weekly is that it makes a great closing technique. The prospective resident is told that rents are adjusted weekly and due to the popularity of this particular style it is likely the rent will increase on Monday. However, fi they would like to leave a deposit today, we can lock them in at the lower rental rate. And it’s the truth! If the unit is in a group with few vacancies, the rent is likely to go up.
Dealing with Skeptics
The system is proven to be effective, but be prepared for resistance form the on-site staff. I discovered this after visiting one of my properties that had been using the new system for 60 days. The manager told me she understood the system might work at other properties, but it just didn’t work at hers.
She explained that three sides of her property adjoined a beautiful golf course, and they had always charged a $20 view charge for the exterior units that overlooked the golf course. She had used the system religiously for 60 days, and what she experienced upset her. She said most of the turnovers had been in the interior units and, because they had rented so well, they had experienced some substantial rent increases.
“Now the interior units are $20 higher than the exterior units,” she said. “Your system doesn’t work at this property!” I said, “the interior units have increased $40 in 60 days, and the system doesn’t work?” She replied that it did not because the most valuable units now cost less than the least valuable units.
I suggested she had just learned the most valuable units were actually the interior units. She personally preferred an exterior unit because she had no children. I suggested that some people prefer to be able to see their small children playing outside in front of the apartment and also perhaps preferred to be able to see their BMW and Mercedes-Benz automobiles in the parking lot and carports. Her paradigm and prejudice had previously restricted rent increases. It was difficult for her to accept the interior units were more valuable and should command higher rent.
Proof is in the Pricing
It takes patience to implement, but the system works because it is based upon simple supply and demand. I use a different benchmark (target the occupancy percentage) for summer and winter for most properties. The benchmark can be changed as often as necessary until you reach an optimum point of rate versus occupancy percentage.
You may be surprised at how quickly rents increase initially. After the system has been used or a year or two, the increases usually become less, however your income stream during that time should increase substantially. It has worked ver well at my properties.
I purchased a property in November that did not stabilize occupancy until one year later in April . One of the motivations for purchasing the property was the belief that rental rates were below market. A market baseline for January was established for comparative purposes by multiplying the average market rent of $590 by the number of units in our property. The market rate was derived by applying percentage increases or decreases as reported in market studies conducted by third-party sources. While market rates have remained relatively flat for the period, rents at the property have increased substantially.
The rent matrix has been used since the purchase of the property. The result has been an increase in value of approximately $1.5 million thus far. These results may not be attainable in all markets, as markets and properties will vary.
Brad Marting, CPM, CAPS, has 27 years experience managing commercial retail, office, hotel, condominium, subsidized and conventional multifamily, and single-family rental investment real estate property. Mr. Marting was the President of Northern Indiana IREM Chapter 100.
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