Since writing about “My Airbnb Experiences: R1,182,670 Later” I have been getting loads of questions about Airbnb – most of them about http://archmdmag.com/compton-west-sussex-metal-detection-event-24th-to-26th-august-finds-from-2016/ Airbnb Cape Town numbers. Not being one for half measures – here is my first official enter Superhost South Africa Cape Town Report. Enjoy.
So let me start off by saying that I am not an oracle – I cannot say with any certainty what the future holds. Let me also mention that I do not work for Airbnb nor Airdna. I am an accountant though and do like me some Excel – so I will attempt to answer some of these questions the only way I know how – by consulting the numbers.
In my previous post I eluded to the fact that I am a bit of a sucker for data. I didn’t mention that I am also a sucker for deals. So when Airdna offered their detailed here Market Intelligence Report which usually costs $1,500 for $375 I could not resist. (You can thank me later).
Airbnb Cape Town Numbers
How many Airbnb listings are there in Cape Town?
10,000 – or that is at least the unofficial number that has been doing the rounds lately. Based on my Airdna data there were 11,549 listings crawled. Of course not all these listings are relevant to our analysis. Of the 11,549 listings, 2,176 listings have made no income in the past 12 months and a further 1,867 listings have no data in respect of income (i.e. no bookings were observed in the 12 month period). That leaves us with diclofenaco 800 mg high 7,506 listings to analyze.
The Average Listing
I am not a big fan of averages (or being average for that matter). In the case of this data set, the averages both understate the upside and massively downplay the downside. But it seems like a logical starting point.
|Room Type||Number of Listings||Annual Revenue LTM||Effective Day Rate||Effective Occupancy|
Looking at the table above is rather meaningless. You would probably like to know where these listings are located, how many bedrooms they have and if they were listed for the whole year. You might also wonder what the top-performing listings are making. So let’s delve a bit deeper into the data set.
Entire Place Breakdown
“Entire Place” listings account for 75% of the number of Airbnb listings and 92% of the revenue in the data set . In addition, Superhost South Africa exclusively manages “Entire Place” listings and not private/ shared rooms. For this reason the remainder of the blog post will focus on “Entire Place” listings.
Number of Bedrooms
As a starting point it makes sense to split listings up based on the number of bedrooms. This makes it possible to formulate some sort of opinion on the revenue and occupancy based on existing knowledge of both selling prices and long-term rental rates.
|Number of Rooms||Number of Listings||Annual Revenue LTM||Effective Day Rate||Effective Occupancy|
Apartments listed as having zero bedrooms account for 421 out of the total 5,667 entire place listings that made income in the last twelve months. The 421 studios generated $1,497,716 revenue in the last twelve months by booking 22,399 nights out of the 48,377 nights available – resulting in an effective day rate of $66.87 and an effective occupancy of 46.3%.
Studio Revenue Spread
As I mentioned before – I am not a fan of averages. On the surface the average annual revenue on a studio apartment is $3,557.52 in the last twelve months. (Bear in mind that this is after removing listings that had zero revenue data.) That is hardly something to get excited about. There are some studios that made substantially more than this (one made more than $30,000).
The problem with the annual revenue data is that not all the listings were listed for the entire year (most were actually only listed for a month or two – see below). In order to address this I also calculated an effective annual revenue which amounts to a much more interesting $11,300.70.
Note: The effective annual revenue figure above assumes all the listings maintained their effective day rate and effective occupancy for the whole year, which is not a reasonable assumption. We might be annualizing high day rates and high occupancy for listings only listed in peak season (or vice versa annualizing low day rates and low occupancy for listings only listed in low season). So it is best to view this figure conservatively.
The Airdna Market Intelligence Report (monthly) reflects monthly listing data for April 2015 to January 2016. As such the reports below (reflecting data for 10 months) do not tie back to the annual figures.
As mentioned above, one of the factors skewing the data is that not all the listings were listed for the entire year. As it turns out most listings were listed for less than 3 months in the last 10 months.
Airbnb listings have seen a steady incline since May 2015, with a rather large spike in January 2016. The steady incline can most likely be attributed to increased awareness whereas the January spike probably relates more to seasonal hosts (hosts renting their places while they go on holiday).
The heat maps below were generated using Google Fusion Tables – plotting the latitude/ longitude of each listing as well as its Annual Revenue LTM (key: red = higher, green = lower)
Cape Town studio apartment hotspots for revenue appear to be concentrated around the CBD and Atlantic Seaboard. Again this data may be skewed by the fact that most early adopters were situated in the above-mentioned areas, whereas other areas might be relatively new to Airbnb.
A closer look at the CBD and Atlantic Seaboard reveals the City Center as a hotspot. The city is filled with many dual-zoned buildings that have traditionally allowed short-term rentals, whereas many buildings in areas such as Sea Point do not allow rentals for less than 3 months.
Top 30 Studios
In order to identify top performers, I filtered all the studio data for those listings that earned an Annual Revenue LTM of greater than $11,300.70 (being the Effective Annual Revenue calculated above). As it turns out only 30 listings out of the 421 analyzed met this criteria. Again the fact that not all listings were listed for the entire years plays a role. On average, studios in the top 30 were listed for 9.3 months out of the last 10.
On average the Top 30 Studios each earned $17,129 in the last twelve months, averaging a day rate of $79 per night and occupancy of 70% (compared to the average on all studios of $67 and 46% respectively).
On average, listings in the Top 30 Studios:
- Took 160 minutes to respond to messages/ bookings
- Hosted 33 bookings in the last twelve months
- Received an overall review score of 4.7 out of 5
- Charge a security deposit of $126 and a cleaning fee of $20
- Check guests out before 10am and check new ones in after 2pm
- Have a minimum stay of 3 nights
- 10 out of 30 hosts have achieved Superhost Status
- All 30 have Instantbook enabled
- 19 out of 30 listings have Strict Cancellation Policies
Download the Full Report
Download the Full Superhost South Africa Cape Town Report February 2016 here. It includes the same analysis from above for one and two bedroom apartments. And it is free… No brainer, right?
There are a few things I know for sure:
- Airbnb has grown tremendously in Cape Town over the past year. What effect this growth will have on pricing and occupancy in the low season remains to be seen.
- The majority of hosts are either seasonal hosts or very new hosts. Both present quite a steep but rewarding learning curve. Checkout the Airbnb Hosts Cape Town group on Facebook if you need some help or join one of the regular Airbnb meet ups.
- Very few people have made amazing Airbnb income (which is completely okay – see “What the data cannot measure” below). Of course this data is skewed by point (2) above.
- Having a top performing listing is hard work. From replying quickly to enquiries, to maintaining a high review score and hosting loads of guests while turning a listing over on the same day – it can become a full-time job.
I hope this post has answered some of your questions about the Airbnb Cape Town numbers. Please feel free to give me a shout if you have any questions about the data or the analysis.
About the Data
What the data cannot measure
I want to start off by saying that there is a whole lot more to Airbnb than the numbers. Being an Airbnb host myself (first to guests in my own home and now to guests in apartments I own) has brought me a whole lot more than just extra income. I have made great friends, learnt to appreciate the beauty of my home town again and started to trust strangers. Unfortunately it is not possible to measure “warm and fussy” in Excel but I promise you there is plenty of that too.
Airdna is amazing
These reports are amazing! The data is practically impossible to obtain yourself. At this stage there is no Airbnb API and their data is closely guarded. Airdna reports are based on Airbnb data gathered from information publicly available on the Airbnb website.
By constantly reviewing the calendar information of Airbnb properties they can determine when a place was booked and for how long. When a new reservation is recorded, they then calculate the advertised daily rate of each of those days directly before the booking occurred and add in the cleaning fee for each unique reservation to determine total revenue from that booking. For more info read this.
My Data Set
I purchased the Airdna Market Intelligence Report in March. This included two data sets:
- Cape Town Properties: a spreadsheet of all Cape Town properties that were crawled for the proceeding 12 months including the following information about each individual property: Property ID, Host ID, Listing Title, Property Type, Listing Type, Last Scraped Date, Country, City, Average Daily Rate, Annual Revenue LTM, Occupancy Rate LTM, Number of Bookings LTM, Number of Reviews, Overall Rating, Bedrooms, Bathrooms, Max Guests, Calendar Last Updated, Response Rate (%), Response Time (min), Superhost Status, Cancellation Policy, Security Deposit, Cleaning Fee, Extra People Fee, Published Nightly Rate, Published Monthly Rate, Published Weekly Rate, Check-in Time, Checkout Time, Minimum Stay, Count Reservation Days LTM, Count Available Days LTM, Count Blocked Days LTM, Number of Photos, Instantbook Enabled, Listing URL, Listing Main Image URL, Latitude and Longitude.
- Cape Town Monthly: a spreadsheet of monthly data from April 2015 to January 2016 for all properties that were crawled including the following information for each property for each month: Property ID, Host ID, Property Type, Listing Type, Bedrooms, Reporting Month, Occupancy Rate, Revenue ADR, Number of Reservations, Reservation Days, Available Days, Blocked Days, Country, State, City, Latitude and Longitude.