Local Demand changes dramatically, even night to night. In metropolitan areas, a local conference can cause demand to rise 100% in a day.
Alternatively, tourist destinations such as Tahoe, California, will often see weekends sell out well in advance, while weeknights remain unbooked.
Given this variability, its important to have a strong predictive model for local demand if you want to capture more revenue during these price peaks.
Hotels & airlines spend millions annually predicting local demand, and undoubtedly the base of Dynamic Pricing is the ability to predict local demand.
Determinants of Local Demand
To build a complete picture of nightly demand, we gather & analyze data from many sources, including:
- Local conferences & events
- Seasonal demand changes
- Day of Week fluctuations
- Local hotel prices & occupancy
- Incoming & Departing flights
- Airbnb booking rates
- VRBO booking rates
All this data helps us develop understand the flow of travelers to & through a region. Also, it allows us to know how strongly the local market for accommodations is performing.
Lets examine how three of these factors impact prices for your listing.
The Impact of Local Conferences
Conferences have a major impact on Airbnb prices. In fact, neighborhoods that are close to conference centers (two miles or fewer) can book out even during mid-sized conferences.
We track every upcoming conference, and try to determine the number of rooms that each conference will book. Additionally, we watch hotel prices & occupancy as conferences approach. This data provides evidence as to how nearby rental properties will book.
Daily number of rooms booked by Conference Attendees
Booking rates for listings due to conferences differ dramatically. The impact of a conference on your listing is primarily determined by your proximity to the venue. Additionally, your listing type & other details (split bathrooms for guests) effect how likely your home is to book during a conference.
Additionally, the time of determines the impact of a conference. For example, a mid-sized conference in the summer, already a high travel time, can have the same impact as a large conference during the winter months.
Price changes based on Conference Size & Distance from Conference
The Impact of Seasons
Many hosts underestimate the impact that high travel season can have on local demand. Similarly, off-season travel rates can drop so precipitously that hosts often think their listing has been penalized by various listing sites.
We determine a region's seasonal demand curve by analyzing flight & hotel data. Additionally, we watch the speed at which vacation homes rent each season, as summer months sees more families traveling, who increasingly look to Airbnb for rentals.
Seasonal Demand Curve based on Region
The Impact of Day of Week
Interestingly, is many areas, the price of a hotel room will peak on Monday & Tuesday. For many hosts, this comes as a surprise. However, this pattern is a result of business travelers, who are often traveling early in the week.
For Airbnb listings, particularly in business-friendly neighborhoods, the impact of the Day of Week can be dramatic. For example, In San Francisco, the SOMA district can often see demand peak mid-week. Pricing these days accordingly help you maximize revenue.
Other neighborhoods, such as the Marina District in San Francisco, follow a more traditional vacation rental weekly demand curve. In these neighborhoods, demand spikes on weekends, and can drop dramatically mid-week.
We calculate each neighborhood's demand curve, to make predictive assumptions about how your place will sell throughout a week.
Weekly Demand Curve based on Neighborhood Type
Our Local Demand Model
Our Local Demand Model adjusts your each nights' prices, based on our predictions for how much demand there will be on any given day.
The goal of the model is to identify periods of increased demand, so you can increase your revenue-per-night and maximize monthly revenue.
To find the right price for each day, our model calculate the overall demand for a day by combining factors that impact local demand. This gives us a demand multiple for each day, which we combine with your Base Price.
Combining factors of local demand to adjust Prices
Why we adjust prices Daily
Our Local Demand model is both predictive & responsive. Each passing day, we gain more certainty about the actual demand rate for a given day.
Pricing daily enables us to react to new events that are announced. For example, when a local college announces its reunion weekend, local demand for that weekend will climb. By watching the actual booking velocity for each day, we can adjust prices on the fly when demand rises.
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