How much should you bid for Phase 3 of the FM Auction?

Dear FM companies, here is your cheat sheet for the upcoming round of the phase 3 FM auctions:

For a 60% chance of winning the auction for a city, bid the amount in the second column. For an 80% chance, bid the amount in the third column. For an almost-certain 95% chance, bid the amount in the fourth column. (I’ll be talking about the statistical models and data collection that went into this in an upcoming post.)

A third of the cities up for auction in the last round of Phase 3 of the auctions went unsold. The cities highlighted in the table below are my predictions for which cities are likely to go unsold in this round.

Ask yourself how badly you want the frequency of a particular city. Refer to the table to find the value that matches your priority for that city. Bid that amount. (All figures in lakhs of rupees.)

City	60% chance	80% chance	95% chance	Reserve Price
Achalpur	237	420	710	171
Agartala	416	599	887	16
Aizwal	342	524	812	12
Akola	375	458	590	30
Alappuzha	272	356	490	702
Amravati	434	517	648	351
Asansol	912	1074	1329	194
Barshi	342	524	812	171
Belgaum	421	504	635	702
Bellary	284	369	504	702

Click here to view and download the full table.

Take Achalpur for example. Maybe you’re very keen on buying the FM station for Achalpur because of say, your business connections there. You’re best off bidding 710 lakhs for a 95% chance of bagging the frequency for Achalpur.

Maybe, unlike Achalpur, you’re not familiar with the business atmosphere in Agartala. Quite naturally, you decide that you want a lower, say 60% shot at winning the frequency for Agartala. In this case, you should look toward bidding at 416 lakh.

So that’s the way you want to bid for every frequency up for auction. But maybe just knowing what price to bid isn’t enough for you. You want to know if you’re getting a bang for your buck. The last column, the reserve price, gives an indication of just that. It’s the price at which an auction kicks off – a ‘base price’ for every city up for auction.

Some cities are overvalued and others are undervalued. For Agartala, the reserve price is far lower than the 60% bid; if you don’t buy it, someone else likely will. It’s reasonably valued. Now take a look at Alappuzha. It’s reserve price is quite high; it even tops the 60% bid. It’s an overvalued frequency and you would not want to buy it unless you’re very interested in Alappuzha. These ‘overvalued frequencies’ are highlighted in the table. My model says that these cities are punching above their weight: their reserve prices are way too high.

I hope this table was of value to you. Good luck at the auctions!

Will Alappuzha ever get an FM radio station?

Alappuzha, a 2 million-people city in Kerala, has a problem. It lacks its own FM radio station. Why’s that a problem? Because this isn’t some small town in the middle of nowhere. It’s a city with a population that demands an FM channel and has more than enough potential to deserve one. Before I talk about why Alappuzha might not get an FM station, here’s some history that will make clear why it matters.

Alappuzha Medical College

There’s always been a lot of demand for an FM channel from the locals. All they have right now is relayed radio from New Delhi and Trivandrum, which they don’t want for obvious reasons. They want their own unique voice – their own FM station. Apart from that, there’s a community called the Kuttanad farmers who are keen on receiving a frequency that will detail conversations with agricultural experts, talk about new projects, subsidies and even allow farmers to air their own content – all of which will undoubtedly help them.

Farmer in a paddy field

Boat Race in Alappuzha

 

 

 

 

 

 

 

So if there’s demand, why haven’t the people and the State government of Kerala appealed to the Center in the past? Thing is, they have. The State government tried to do just that in 2006, 2009 and 2010; all three proposals were rejected. The Center said that they’ve already allocated all available frequencies to private companies who aren’t using those frequencies to broadcast anything. Basically, no slots and no FM. So far, save for the one relaying station AIR set up all the way back in 1971, the FM scene in the city has been dry.

Still, maybe it’s all in the past and Alappuzha will finally get an FM station in the upcoming round of auctions. After all, Phase 3 is supposed to spread the reach of radio to Category C and D small towns. But will they really get an FM station? My model predicts that the frequency for Alappuzha has only a 17% chance of getting auctioned. It seems a little pricey at a minimum price of 4.23 crores, given that cities like Guwahati, Agra, Surat and Allahabad sold for less.

If it’s any consolation, frequencies for Eroda, Salem, Vellore, Malegaon and Latur are also likely to go unsold. Citizens of Alappuzha: I’m sorry but your wait for an FM station just got longer.

Every frequency has a price at which companies will snap it up. This one is just a bit too high.

Predictions for FM Auction Phase 3

FM auctions for over 200 cities are starting tomorrow. Since 45 of 135 auctions failed the last time round, I tried to take a crack at finding which auctions will fail this time. This blog post is about my predictions for the second round of Phase 3 of this auction. Let’s see what happens when the actual auction data rolls in.

The cities least likely to get auctioned according to my model: Alappuzha, Eroda, Salem, Vellore, Malegaon, Latur, Bijapur, Bellary, Barddhaman, Baharampur, English Bazar, Abohar, Thanjavur, Dindigul, Kanhagad, Karaikkudi, Karur, Neyveli, Pudukkottai, Rajapalayam, Tiruvannamalai, Bharuch, Junagadh, Mehsana and Yavatmal. These cities have a lower ‘ratio’ because the model deems the reserve prices of these cities to be too exorbitant.

The cities most likely to get auctioned according to my model: Saharanpur, Muzaffarnagar, Ujjain, Ambala, Tinsukia, Nellore, Dhanbad, Siwan and Ludhiana. These cities have a higher ‘ratio’ because their reserve price is quite reasonable compared to the prices they’re expected to sell at.

I have built this model based on the ‘Category’, Domino’s pizza outlets, Cafe Coffee Day stores and Hero MotoCorp dealerships in each of these cities – a proxy for the paying capacity of consumers in that city.

The recommended bid is the value (in lakhs of rupees) that a company should want to start bidding at, and Max Bid is where the company should stop bidding. The last column indicates the chance of success of the auction.

Take Abohar for example. The reserve price is a good way away from the Maximum bid for its frequency – an indicator that the auction will likely succeed. Now, do the same for Alappuzha. The Reserve price is at the heels of the Maximum bid price. This is an indicator that the auction will likely fail.

These are my predictions for the second round of the Phase 3 FM auctions:

City	Reco Bid (in lakhs)	Max Bid (in lakhs)	Reserve Price (in lakhs)	Probability of auction success
Abohar	12	677	135	0.81
Achalpur	50	835	124	0.91
Adilabad	21	748	110	0.88
Adoni	50	835	110	0.92
Alappuzha (Alleppey)	141	480	423	0.17
Alipurduar	50	835	110	0.92
Alwal	50	835	110	0.92
Alwar	125	792	214	0.87
Ambala	292	992	123	1.24
Amravati	292	577	310	0.94
Anantpur	192	921	110	1.11
Arrah	12	677	39	0.96
Azamgarh	192	921	58	1.18
Baharampur	21	748	214	0.74
Bahraich	21	748	58	0.95
Baleshwar	21	748	58	0.95
Ballia	21	748	58	0.95
Balurghat	21	748	110	0.88
Bands	21	748	58	0.95
Bangaon	50	835	110	0.92
Bankura	21	748	110	0.88
Barddhaman	12	677	214	0.70
Baripada	21	748	58	0.95
Barshi	21	748	124	0.86
Basti	21	748	58	0.95
Beawar	21	748	110	0.88
Begusarai	12	677	39	0.96
Belgaum	279	565	268	1.04
Bellary	154	452	268	0.62
Bettiah	21	748	39	0.98
Bhadurgarh	12	677	123	0.83
Bhagalpur	154	452	105	1.16
Bharatpur	192	921	110	1.11
Bharuch	12	677	124	0.83
Bhatinda	234	926	135	1.14
Bhavnagar	268	553	310	0.85
Bheemavaram	21	748	110	0.88
Bhilwara	125	792	214	0.87
Bhiwani	12	677	123	0.83
Bidar	21	748	99	0.89
Bihar Shareef	21	748	39	0.98
Bijapur	12	677	268	0.61
Bokaro Steel City	12	677	39	0.96
Botad	39	760	124	0.88
Brahmapur	125	792	130	0.99
Budaun	21	748	58	0.95
Burhanapur	21	748	66	0.94
Chapra	21	748	39	0.98
Chhattarpur	21	748	66	0.94
Chhindwara	12	677	66	0.92
Chikmagalur	24	727	99	0.89
Chirala	21	748	110	0.88
Chitradurga	21	748	99	0.89
Chittoor	41	745	110	0.90
Churu	21	748	110	0.88
Coonoor	39	760	150	0.85
Cuddapah	12	677	110	0.85
Daman	21	684	124	0.84
Damoh	21	748	66	0.94
Darbhanga	12	677	39	0.96
Darjiling	41	702	110	0.90
Dehradun	446	741	448	0.99
Deoghar	21	748	39	0.98
Deoria	21	748	58	0.95
Devengeri	268	553	268	1.00
Dhanbad	690	1181	580	1.22
Dharamavaram	50	835	110	0.92
Dibrugarh	44	731	25	1.03
Dimapur	21	748	25	1.00
Dingdigul	21	748	150	0.82
Dohad	21	748	124	0.86
Durg-Bhillainagar	12	677	42	0.95
Eluru	21	748	110	0.88
English Bazar (Maldah)	21	748	214	0.74
Erode	268	553	423	0.46
Etah	21	748	58	0.95
Etawah	21	748	58	0.95
Faizabad/Ayodhya	21	748	58	0.95
Farrukhabad cum Fatehgarh	21	748	58	0.95
Fatehpur	21	748	58	0.95
Gadag Betigeri	50	835	99	0.94
Ganganagar	12	677	110	0.85
Gaya	154	452	105	1.16
Ghazipur	21	748	58	0.95
Giridih	21	748	39	0.98
Godhra	21	748	124	0.86
Gonda	21	748	58	0.95
Gondiya	21	748	124	0.86
Guna	21	748	66	0.94
Guntakal	50	835	110	0.92
Haldwani-cum Kathgodam	44	731	150	0.85
Hanumangarh	50	835	110	0.92
Hardoi	192	921	58	1.18
Hardwar	79	766	150	0.90
Hassan	21	748	99	0.89
Hazaribag	12	677	39	0.96
Hindupur	50	835	110	0.92
Hoshiarpur	68	756	135	0.90
Hospet	21	748	99	0.89
Hubli-Dharwad	314	601	268	1.16
Imphal	21	748	67	0.94
Itarsi	50	835	66	0.98
Jagdalpur	21	748	42	0.97
Jamnagar	268	553	310	0.85
Jaunpur	192	921	58	1.18
Jetpur Navagadh	50	835	124	0.91
Jhunjhunun	21	748	110	0.88
Jind	21	748	123	0.86
Jorhat	12	677	25	0.98
Junagadh	12	677	124	0.83
Kaithai	12	677	123	0.83
Kakinada	268	553	214	1.19
Kanhangad (Kasargod)	21	748	150	0.82
Karaikkudi	21	748	150	0.82
Karimnagar	192	921	110	1.11
Karur	21	748	150	0.82
Kavarati	50	835	5	1.06
Khammam	21	748	110	0.88
Khandwa	12	677	66	0.92
Kharagpur	44	731	110	0.90
Khargone	21	748	66	0.94
Kohima	50	835	25	1.03
Kolar	59	763	99	0.94
Korba	125	792	42	1.12
Kothagudem	21	748	110	0.88
Krishnanagar	21	748	110	0.88
Kurnool	267	560	214	1.18
Lakhimpur	192	921	58	1.18
Lalitpur	21	748	58	0.95
Latur	12	677	310	0.55
Ludhiana	1091	1581	989	1.21
Machillpatnam	50	835	110	0.92
Madanapalle	50	835	110	0.92
Mahbubnagar	24	727	110	0.88
Mahesana	12	677	124	0.83
Mainpuri	21	748	58	0.95
Malegaon	141	480	310	0.50
Mancherial	21	748	110	0.88
Mandsaur	21	748	66	0.94
Mathura	175	867	58	1.17
Maunath Bhajan (Distt. Mau)	192	921	58	1.18
Mirzapur cum Vindhyachal	21	748	58	0.95
Moga	68	756	135	0.90
Moradabad	685	1235	576	1.20
Motihari	21	748	39	0.98
Munger	21	748	39	0.98
Murwara (Katni)	21	748	66	0.94
Muzaffarnagar	267	560	130	1.47
Nagaon (Nowgang)	21	748	25	1.00
Nagarcoil/Kanyakumari	192	921	150	1.06
Nalgonda	41	745	110	0.90
Nandyal	21	748	110	0.88
Neemuch	12	677	66	0.92
Nellore	279	565	214	1.23
Neyveli	21	748	150	0.82
Nizamabad	192	921	214	0.97
Ongole	21	748	110	0.88
Orai	21	748	58	0.95
Palakkad	125	792	150	0.96
Palanpur	21	748	124	0.86
Pali	21	748	110	0.88
Panipat	151	842	123	1.04
Patan	21	748	124	0.86
Pathankot	116	806	135	0.97
Porbandar	21	748	124	0.86
Portblair	21	748	25	1.00
Proddatur	21	748	110	0.88
Pudukkottai	21	748	150	0.82
Puri	44	731	58	0.98
Purnia	21	748	105	0.88
Puruliya	21	748	110	0.88
Rae Barelli	125	792	58	1.10
Raichur	21	748	99	0.89
Rajapalayam	21	748	150	0.82
Ramagundan	50	835	110	0.92
Raoganj	21	748	110	0.88
Ratlam	12	677	66	0.92
Rewa	12	677	66	0.92
Rewari	44	731	123	0.88
Rohtak	44	731	123	0.88
Sagar	154	452	140	1.05
Saharanpur	292	577	130	1.57
Saharsa	21	748	39	0.98
Salem	267	560	423	0.47
Sambalpur	12	677	58	0.93
Sasaram	21	748	39	0.98
Satna	12	677	66	0.92
Sawai Madhopur	21	748	110	0.88
Shahjahanpur	154	452	130	1.08
Shimoga	192	921	268	0.90
Shivpuri	21	748	66	0.94
Sikar	192	921	110	1.11
Silchar	12	677	25	0.98
Singrauli	50	835	66	0.98
Sirsa	12	677	123	0.83
Sitapur	192	921	58	1.18
Siwan	192	921	39	1.21
Sultanpur	192	921	58	1.18
Surendranagar Dudhrej	50	835	124	0.91
Thanesar	39	760	123	0.88
Thanjavar	24	727	150	0.82
Tinsukia	192	921	25	1.23
Tiruvannamlai	21	748	150	0.82
Tonk	21	748	110	0.88
Tumkur	199	893	99	1.14
Udupi	50	835	99	0.94
Ujjain	267	560	140	1.43
Vaniyambadi	50	835	150	0.87
Vellore	279	565	423	0.50
Veraval	21	748	124	0.86
Vidisha	21	748	66	0.94
Vizianagaram	12	677	110	0.85
Wardha	21	748	124	0.86
Yavatmal	12	677	124	0.83

A probability greater than 1 in the last column indicates a 100% chance of the frequency getting sold.

Some data about the reserve prices wasn’t available, so what follows is an FM prediction for certain cities in case they do go up for auction:

City	Reco Bid (in lakhs)	Max Bid (in lakhs)
Amritsar	1009	1503
Asansol	607	1109
Bhopal	1196	1689
Chennai	10842	12121
Coimbatore	1295	1802
Gangtok	41	702
Gulbarga	154	452
Gwalior	303	588
Indore	1359	1867
Jabalpur	760	1253
Jalandhar	705	996
Kannur	188	504
Kolkatta	9868	11389
Mangalor	539	827
Mysore	622	911
Pannaji	44	731
Pondicherry	410	699
Raipur	611	901
Rajamumdry	267	560
Rajgarh	50	835
Ranchi	637	956
Shimla	41	702
Siliguri	373	661
Tiruchy	279	565
Tirunelveli	154	452
Tirupati	154	452
Tiruvananthapuram	242	545
Trissur	448	745
Tuticorin	141	480
Vadodara	1253	1759
Vijayawada	784	1276
Visakhapatnam	760	1253
Wadhwan (Surendernagar)	21	748
Warangal	267	560

I’ll talk about how I built the models that went into this prediction, the data collection behind the variables I used, the conclusions I drew from this model and how this model scales up to the actual auction in the next few blog posts. I’ll also talk about how I constructed the ratio that judges whether a frequency is fairly priced or not. Stay tuned.

Predicting FM Auctions (Phase 3) – Part 1

The second batch of Phase 3 FM Auctions in India is to take place tomorrow. This means that radio spectrums for over 200 cities are going to be up for auction. Now, what’s a spectrum? And how can a radio company broadcast its content in that spectrum? A spectrum, the same as a frequency, is a slice of a city’s bandwidth. You can think of a frequency as a proportion of the city’s capacity to broadcast.

Buying one of these spectrums allows a radio company to air content in that city. For example, if a city has 7 frequencies up for auction and we go ahead and buy one, we earn the right to broadcast in that city along with 6 other companies – provided that every frequency sells.

Tomorrow, all these frequencies are going to be up for auction and bid on by big players in the Indian radio market such as Entertainment Network India and HT Media, who shell out crores of rupees for frequencies. And why would a company pay this much for rights to broadcast in a city? Because advertisers pay them huge money for those 30-second segments that feature in between songs on the radio.

My only real exposure to FM radio is having it play in the background of cab rides (my family and I prefer to talk), but the idea of being able to maybe predict something – to essentially ‘see’ the future, got me quite excited. That’s why I decided to try and predict the prices these frequencies would sell at before the auction happened. This is what I’ve been working on over the past few weeks. I found the results for the first batch of Phase 3 auctions here and built a linear regression model trained on this data. Then, I went on to use this model to predict the prices that these 200 plus frequencies would sell at.

The next few blog posts are going to be about this process of model building, finding out how much these private companies should bid and whether certain frequencies are going to get sold at all or not. These new blog posts might also be about me hitting way off the mark and trying to learn why my predictions were wrong. I’ll find out soon enough. I’ll head back to recommender systems once I successfully build and test this model. And once I see how it compares to the actual prices these frequencies get sold at.

Building a model that predicts where Domino’s pizza should open

Picking up where I left off on my last blog post, I decided to work on Domino’s Pizza and see what I’d find. I got to work and applied user-based collaborative filtering to my franchise data of store locations for Domino’s Pizza, Eicher Motors, Hero MotoCorp, Cafe Coffee Day, Inox Movies and V-Mart from back in 2015. I found a package called recommenderlab in R and (I’ll spare you the technical details) built a model to recommend locations for franchise openings. I was prepared to run my program right away, but first I had to make sure this model could predict franchise locations at least somewhat accurately. I had to make sure my program worked.

To do this, I took store location data for Domino’s Pizza that I’d collected back in 2015 and ran my model. It told me that Bharatpur, Chittorgarh and Palakkad were the top 3 towns Domino’s should open in. I went onto the Domino’s locations web page to see if I’d struck gold. And I had. One year later, Domino’s now has stores open in Chittorgarh and Palakkad.

This really excites me because if this holds true for other franchises, then maybe I’ll know where franchises are going to open before they do. And maybe, to some degree,  I’ll be able to recommend cities for companies to open in.