Arseniy Feshchenko, the founder and product designer at bird, has spoken to AIN.Capital about what is happening now with the Ukrainian capital’s real estate market, how bird is doing, and how its London analog was launched.

“We didn’t know what would happen to the rental market and when the usual housing rental would be back”

When the war started, I think like everyone else in Ukraine, we were in a state of uncertainty. At that time, our utmost priority was, of course, taking care of our families, our health, our safety, and helping Ukraine. That is why, in March, we concentrated on that not just at bird but also at LUN. It is impossible to work on creating a great product when a nearby building is being bombed. 

By the end of March, we realized that we wanted to switch back to what we could do best. In bird, what we can do best is the rental business, and we also know how to fly through difficult times: we were over this during the pandemic.

On February 22, two days before the war began, we gathered in the office to present our new plan of action, which was to focus on the European market while not neglecting the Kyiv market. The war began, but the plan did not change. On the contrary, we became even more convinced of its correctness. We realized that our great Ukrainian product should enter global markets, at least, to minimize risks.

So we decided that our goal number one was London and birb.

“The first thing we did in the Kyiv bird was turn off all notifications”

When people think about how to get on with their lives, how to fuel the car, and where to sleep tonight — they surely don’t need to know that bird has a new apartment available. This is no longer relevant. It distracts you.

We also added the direction for the Russian ship to go onto the bird map. This is maybe an emotional feature, but it illustrates our position. If you zoom out to a very small scale in the app, you can see both the inscription and the Ukrainian territory. I think it is important to indicate your position, and for many people it is key to know that the products they use are Ukrainian and pro-Ukrainian.

We added two important functions, which we put a lot of faith into. One is to help Ukraine’s Armed Forces — donate to our army directly from the app. I believe that the more opportunities like this our citizens have, the better, because every hryvnia brings us closer to victory. Since the beginning of the war, the donation button has been used by 5,000 people. We still have a modified button like that in the app.

We introduced a button for seeking refuge. So, people who moved to Kyiv from other places could use this button to access a portal with information on temporary housing. As many as 29,000 users have used this option. As of now, when the issue of temporary shelters is not as pressing, we have hidden the button to focus on long-term rentals.

We put bird select on halt and hid all the apartments there. This is a section where we had marketed special apartments with video room tours in the middle+ segment. Those apartments were quite expensive. After the war began, this section became largely irrelevant because the demand for rental housing took a steep dive. So, we turned it off. But now that the market is recovering, we plan to relaunch this section.

“In May, the rental market in the capital recovered by half”

After the beginning of the war, the demand fell dramatically. The number of ads themselves also dropped. From 50,000 to 10,000, that is, tenfold. How could you see it in our app? Houses with available apartments are highlighted on the bird app, and the map was almost empty at the beginning of the war.

But we noticed that in a few months, when the mist of uncertainty cleared away, people people started coming back to Kyiv, although Klitschko had asked them not to. We started notice that by the number of people looking for an apartment. And that was a very good sign for us — the market was gradually recovering.

On May 10, we marked an important figure — the market has recovered by half. That is, 50% of the people who were looking for an apartment before the war are looking now. At the same time, the median price dropped by almost 40%. The number of listings has also started growing substantially, and now there are 70,000 of them. Right now, demand for rent has resumed by 85%, so we plan to restore the bird select section.

“The Kyiv app was launched in London on the Kyiv Day”

Since then, the London app has been downloaded by over 1,000 users, and it is used by about 100 people a day. These figures are important for us because we have a new product ‘hatched,’ and for a young product, being used is crucial. Based on feedback from the first users, we will draw conclusions on how to improve the app.

Naturally, bird has greater results in Kyiv: 10,000 a day. So, our goal is to make sure that the London app is downloaded by 10,000 people in June.

At the same time, there are already 50,000 apartments and 70,000 listings on birb. Now we are bringing in ads from nine major real estate portals, but not yet from agencies. In the future, we will expand this list of ad resources.

“We’re going to monetize birb in a very clear way”

The first step is to make a great product that people love — enjoy using it to look for accommodation and recommend it to their friends. That is what we did in Kyiv — we’ve created a good product used by many people in Kyiv. Only after that we added the option of monetizing, the bird select section. We consider it as one of the monetization options in London.

But there are interesting local market features in London that can be used for monetization too. For example, there are a lot of Built-to-Rent buildings. These buildings are only for rent; they don’t have apartments for sale. The first years after construction, they’re only supposed to be rented by law. So we would like to adapt some features on our app specifically for Built-to-Rent buildings. It seems like it could be a great adaptation of the bird select feature.

“Our team is used to planning not just how to conquer the world, but to make thoughtful realistic plans”

We don’t plan longer than half the time of the product’s existence. bird is three years old, and it would be weird to plan ten years in advance. That’s why we do it for a year and a half. In the coming months, it will be an active development of the London product and restoration of the bird select section in Kyiv.

“The rental search app Birb is launching in London, in one of the most difficult and competitive markets. Birb’s team is from Ukraine, and they continue to work and support the Ukrainian economy,” the company reports.

• According to the developers, while using many rental apps, it is quite challenging to find a location of an apartment, and maps in such apps are not always user-friendly.
• Birb’s developers focused on this issue and built an app based on a mobile map with the addition of artificial intelligence that removes duplicate listings, adjusts images, etc.
• According to the team, the app features ads from agencies (realtors) now. But in the future, the company plans to make it possible that the renter does not interact with intermediaries or owners at all. The app will work the following way: You watch online viewing in the app, tap Rent this flat, check-in with a digital key from the app.

The AIN.UA journalist has talked to Arseniy Feshchenko, bird founder and product designer, and learned why LUN needed another apartments search engine, how it develops, and why it will not expand within Ukraine, working only in Kyiv.

How and Why an apartment rental app bird was created

It began in 2017. Every Friday at lunch, a team of four people, including Denis Tsyganok, LUN co-founder, and Arseniy Feshchenko, designer at that time, gathered and dreamed of how the search of the apartment will look in the future. Then they finally visualized their ideas and developed a concept in 2020. They imagined an app where users can choose apartments on a 3D map only. There will be no lists. You can virtually visit each location and buy the favored one for hundreds of thousands of dollars, using Touch ID.

At that time, LUN already has had the Flatfy product for Ukraine — an apartments search engine for the secondary market where you can find an apartment within Ukraine to buy or rent it. So, they decided to do some experiments and create a “future app” based on the 2020 concept under an entirely new brand and for the city of Kyiv only — bird.

“It was much harder to develop and launch an app for LUN or Flatfy because their users would have high expectations. And they started a new product — an apartment rental app bird. Nobody has known it. So, there were no expectations. When there are no expectations, the start is much easier,” said Arseniy Feshchenko, the founder and product designer of bird.

bird app rental features for apartment search

bird works like an aggregator — it collects ads from 24 different real estate rental platforms, including DOM.RIA, Country.ua, OLX, Rieltor.ua, 100rielty.ua, and other smaller agencies. A crucial element of the app and the main part of the search is the 3D map. “As an opposite to websites with a list of apartments and addresses, streets, and metro stations. To understand where they are, you need to copy and paste the address into Google Maps. This all was the main problem we wanted to solve with the map as the biggest advantage of bird,” explained Feshchenko.

The app was launched in December 2018. The first version contained no filter or other navigation buttons — it had only the 3D map with highlighted houses where someone leases apartments. We used the Mapbox solution to create it because Apple and Google Maps could not display a huge number of objects and select some of them.

New features arose after users delivered feedback and their wishes in App Store or via Telegram to the bird team. So, soon after the start, developers added room numbers, budget, renovation, and favorite filters. Now there are coming two most desired filters — “pet-friendly” and “children-friendly.”

In 2019, the app won two Red Dot nominations — Interface & User Experience and Apps. However, Arseniy said that he is not satisfied with the slider bar — a solution that allows you to set a price range by the search:

“The users wanted it so much, but we tried to postpone its release as much as possible. We spent about a year studying it fundamentally and did create it. Because ‘price from’ is a very doubtful parameter. We learned that users want to skip fake ads and irrelevant apartments using it.”

Bird AI — a solution for eliminating fakes

The minimum value of the ‘price from’ is currently equal to 6,000 UAH (approx. $225). Our team wanted to make it better and presented an AI-driven solution on the second bird anniversary. It can check ads and discover fake ones: “One day, we realized that we want to be more than just an apartment search app for the users. That we want to help them all the way during the renting process.”

Thanks to a set of 25 parameters, Bird AI can up to 95% certainty discover fraud ads to be removed from the app. Bird AI was developed in cooperation with the LUN team that specializes in developing AI solutions called Flair. “We trained bird AI to find fake ads according to our database of fakes that we collected manually. It was a pretty complicated task because even a sophisticated real estate expert can’t always find all of them,” Feshchenko recalls.

In addition, bird AI looks for all the ads for the same apartment on different platforms, creating a price range on their basis, and filters proposals with “granny-style design.”

Select function and product monetization

In July 2021, bird has got an update with a new feature in the app — select. It is a section with manually filtered freshly renovated apartments in new buildings with moderate+ prices — from $800. The best thing about the bird select feature is video room tours in the story format. According to Feshchenko, they should replace offline visits because visiting apartments is a big pain in the ass for realtors.

Another problem here could be the communication with realtors. To minimize it, you can book an apartment tour in the bird select section by pressing the button “Book a room tour.” After that, you can schedule your visit via Telegram.

bird select now is the single way to monetize the app. The realtors pay a fee of 12.5% of each rental deal. Currently, there are about 30 apartments in this section. We plan to develop and extend bird to other markets. The app will not work in other cities of Ukraine.

bird is a product that encourages you to break new ground. In Ukraine, we already have a successful app Flatfy and understand what other Ukrainian cities need. We want to make expansions to new markets where we will face many new challenges for sure. At the moment, the development of an Android version or adapting it for iPad is not on the list. Arseniy confirmed the iOS version priority: “We want to move at the maximum speed — that is our motto.”

How did the idea appear?

In LUN they say that the company has a huge amount of data on the real estate market of Ukraine. The service has long collected statistics on various parameters: the history of price changes in the primary and secondary markets, the speed of sales and resale of apartments, and so on, up to information about what materials for wall insulation are preferred by developers in a certain price category.

“Now, when urban issues are becoming increasingly public, we see the need to share knowledge with the public. We want to talk about what people care about when choosing an apartment: about the indicators of the houses themselves – height, density and age of building, as well as the quality of life in the areas. These are safety, environmental friendliness, transport accessibility and the dynamics of infrastructure development.”

Where will the data be taken from and how will it be analyzed?

Among the sources of LUN are information from developers, as well as state and third-party resources — the State inventory, norms of the State Architectural and Construction Inspectorate, reports of the Department of Statistics, the Open street map service and so on. “The problem is that, individually, these resources do not give a complete picture. LUN learned how to collect information from different sources and process it,” they say in the company.

For completion of the analysis, LUN cooperates with the Geo Design company, which uses its groundwork to interpret data and compile mathematical models of calculations. Besides that, the service plans to work together with Kyiv urbanists, who will be able to use ready-made infographics for analysis in the sphere of urban sciences.

Why does the company need it?

LUN comments on this as follows:

“The project “LUN Mіsto” has a mission to generate demand for quality housing. The infographics are visual aids for those who choose an apartment. They reflect the quality of life in a given area. Already, the demand for real estate is not just “price per square meter,” people realize that they want to live in a comfortable and pleasant environment.

Most developers are struggling to reduce the cost per square meter, instead of improving the quality of housing. Their ads are no different from the slogans of supermarkets that sell milk at low prices. But the apartment is not a box of milk, it’s a place where a person will live.

Thanks to the “LUN Mіsto” infographics, people will be able to understand which type and where housing in Kyiv meets their requirements. This approach to choosing an apartment changes the market as a whole and increases the demand for quality housing. Developers can no longer build high, dense and cheap apartment complexes, they will need to build it so that people want to live in such houses.”

In November, LUN launched the first such information project about Kyiv, a noise pollution map. With the red color on the map, the company shows which streets have more noise and which have less noise. In LUN they point out that this opportunity has become important for a certain type of users, for example, families who want to improve their living conditions when they decide to have children. Now about 30 thousand people use the noise pollution map.

LUN began as an advertising search engine: it collected and processed information from other sites. “We use various kinds of information processing algorithms starting from the first day of the project’s existence, that is, for more than 10 years. Back in 2008, we implemented the first ad analysis system that used simple “if that” rules to find duplicate ads,” says Volodymyr Kubitskyi, the head of the team that works on AI-solutions. Fully neural networks were started to be introduced in LUN only in 2014, since then its number has exceeded two dozen. To support existing neural networks and work on new AI/ML-algorithms in the company, they created the Flatfy & Lun Artificial Intelligence Researchers team, FLAIR.

Outdoor advertising AI

They remembered about the developments of the team only this summer. In June, the company conducted an advertising campaign called “Do they build or only advertise?”. It promoted a new feature on the site – checking the reliability of the developer when buying an apartment in a new building. Within the framework of the advertising campaign, advertising boards were also planned, and it was necessary to find locations for them. According to Andriy Mima, co-founder of LUN, usually in such cases advertising agencies work according to one pattern: they send a list of several hundred selected advertising spots from which you need to choose, for example, 50. “I call it an “advantage”, like you can choose to your own taste.  And to make a choice, you only have the address of the board, its old low-quality photo and a strange OTS figure. And if you ask to make a choice for you, then they choose the most beautiful ones “by photo”— says Mima.

The company has decided to act differently and asked the agency to shoot a video with the boards, so that it was possible to determine whether the advertisement was clearly visible, whether tree branches or other boards were not obscuring. Then the company created a table with parameters for each board, which would make it easier to decide on each location. The main parameters for each board were identified as:

• board size;
• real picture (that shows whether the trees obscure the sign);
• time of non-visibility;
• competition with other boards in sight.

This label was created by viewing the video prepared by the agency, and manually writing out all the numbers. As a result, they managed to select 50 boards from the proposed 500 ones. However, the team had to spend several weeks on watching the video and filling in the table. Therefore, the next time the process was decided to automate:

• in order to analyze any number of ad slots,
• to increase the speed up such analysis.

At this stage, work with neural networks began. Just at the same time, the FLAIR team worked on a system for determining watermarks in photos of apartments, in order to select pictures without such signs for the title photo in search results. As a result, a system of localization of the object in the photo was created: neural networks showed several examples of what they would like to find in the photo, and then it learned to search basing on the new data itself.

“Just at that moment, the marketing guys were busy selecting sites for boards. We saw each other in the corridor, someone spoke a word about how they learned to detect watermarks and can generally find any object on any photos. So, the idea was to try to analyze the video from the registrar to find the boards,” says Volodymyr Kubitskyi.

According to him, the solution to search for advertising space was created from scratch. “For the implementation of the neural network, the tensorflow framework from Google was used to simplify the solution of the localization problem. In the transition from localization to tracking (localization is single-frame, and the board is contained in many video frames and should not be lost) they wrote their own post-processor,” he says.

How a neural network works

As a result, they managed to create a neural network that can accept video of almost any quality, in any light and at any shooting angle, and determine all ad slots on it, calculate the specified parameters for them: size, visibility time and competing boards that are located beside. This data is entered into a table, and then the marketing team can select any data evaluation formula and filter the results by it. For example, in the summer campaign, the formula looked like this:

Effect = Board area x Time in visibility zone / Number of other boards on the screen.

According to Kubitskyi, the neural network works this way:

• The input video is streamed into a set of frames (usually 24 frames per second) using OpenCV.
• Then the neural network begins to work with the Faster R-CNN architecture, trained to find the boards on the frames: the network gives the answer for each frame in the form of the coordinates of the found boards, if any.
• After this, the post-processing stage begins, where you need to reassemble the video from the frames and understand that the same board is tracked from frame to frame, and a new board is not tracked every time. Here, computer vision algorithms are connected to compare images and a Kalman filter for working with the found coordinates from frame to frame. Since the neural network has learned to work with multiple boards simultaneously, it calculates whether competing boards are in sight.
• All of this data is summarized in the report

According to Andriy Mima, the capabilities of such system can be broader than the current prototype. For example, you can show specific boards to the system and it will automatically find them on video, select them and make an assessment on the parameters. This can be useful for monitoring ongoing ad campaigns.

“I read somewhere that there are 1,600 km of roads in Kyiv. With an average speed of 16 km/h, you can drive around all of them with 3 cars in 4-5 working days, and if you remove the small roads, then, most likely, this task will still be a couple of times easier and cheaper. However, what to do with 100 hours of video then? It is 360,000 seconds, where you need to stop every 5-10 seconds, note the time, count the area and write it down in the table? It is necessary to measure about 50,000 structures, and it seems that this can take several months. It’s a huge amount of work that we have just automated,” says the co-founder of the company.

If it turns out that there is a demand for such technology among advertising agencies or sales houses, the company is ready to sell or license it.