This project is a treatise on Organic Search and Natural Language Processing search for Housing. How can Data-driven design help drive in conversion for Housing and help in market positioning of the brand.

1. Your Identity. Your Home
1
GENE
e
Agrima Nagar | NID | Housing.com© AgrimaNagar

2. Information and Interface Design
Dr. Bibhudutta Baral
Masters in Design ( M.Des )
Agrima Nagar
Sponser : Housing.com
Genie Search - Personal assistant
for house search
2 of 2

3. The Search for truth is a search for Idenity,
that in truth we find Ourselves
Neil Sutton

4. Originality & Copyright Statement
Originality Statement
Copyright Statement
7
I hereby declare that this submission is my own work and it contains no full or substantial copy of previously
published material, or it does not even contain substantial proportions of material which have been
accepted for the award of any other degree or final graduation of any other educational institution, except
where due acknowledgement is made in this graduation project. Moreover I also declare that none of the
concepts are borrowed or copied without due acknowledgement. I further declare that the intellectual
content of this graduation project is the product of my own work, except to the extent that assistance
from others in the project’s design and conception or in style, presentation and linguistic expression is
acknowledged. This graduation project (or part of it) was not and will not be submitted as assessed work in
any other academic course.
I hereby grant the National Institute of Design the right to archive and to make available my graduation
project/thesis/dissertation in whole or in part in the Institute’s Knowledge Management Centre in all forms
of media, now or hereafter known, subject to the provisions of the Copyright Act. I have either used no
substantial portions of copyright material in my document or I have obtained permission to use copyright
material.

5. Acknowledgements
1
I have taken efforts in this project. However, it
would not have been possible without the kind
support and help of many individuals and
organizations. I would like to extend my sincere
thanks to all of them.
I am highly indebted to Bibhudutta Baral for their
guidance and constant supervision as well as for
providing necessary information regarding the
project & also for their support in completing the
project.
I would like to express my gratitude towards
members of Housing.com for their kind co-
operation and encouragement which has helped
me in the completion of this project.
I would like to express my special gratitude and
thanks my team @ Housing for giving me such
attention and time.
My thanks and appreciations also go to my
colleague in developing the project and people who
have willingly helped me out with their abilities.

6. Content
Introduction 22
63
210
About National Institute of Design
About Information and Interface Design
About Housing.com
User Research
Personas and Scenarios
Stratergy and Scope
Secondary Research
Background Research
Primary Research
User Needs
Vision
Business Goals ( Scope )
Analysis
Finding and InsightsSynopsis
Design Process
Setting up the project
Evolution of Ideas
Project Finalisation
Project Brief ( Design Brief )
7
10
18
2

7. Structure 261
268
218
226
250
262
Task Flow
Information Architecture
Personal Assistant
Wireframes
Visual Design
Looking Forward
Conclusions
Biblography
Content
3

9. Introduction
About National Institute of Design
About Information and Interface Design
About Housing
5

10. Housing.com lists properties submitted by users, either
brokers or owners, on an interactive map. Search
results are filtered by available rooms, lifestyle ratings,
child friendliness index (CFI), and area-based pricing.
The company has mapped approximately 650,000
houses in India.
Housing.com's Data Science Lab (DSL) has generated a
number of "Heat Map" algorithms and demand flux.
HOUSING
6

11. The National Institute of Design R&D Campus (राष्ट्रीय
डि ज़ाइन संस्थान) better known as NID - Bangalore
Campus is India's premier design institute located
in Bengaluru, in Karnataka. The R&D campus
specializes in Research and Development activities
related to design and is one of the three campuses
that is part of the National Institute of Design,
Ahmedabad. NID is recognized by the Department
of Scientific and Industrial Research under Ministry
of Science and Technology, government of India, as
a scientific and industrial design research
organization.
NID BANGALORE
7

12. It is the practice of presenting information in a
way that fosters efficient and effective
understanding of IT. The term has come to be
used specifically for graphic design for displaying
information effectively, rather than just
attractively or for artistic expression. Information
design is closely related to the field of data
visualization and is often taught as part of graphic
design courses.
Information & Interface Design
8

14. 10
Synopsis
Setting up the project
Evolution of Ideas
Project Finalisation
Project Brief ( Design Brief )

15. Real Estate deals with more than just e - commerce,
it deals with emotions, memories not just of a single
individual but it also involves multiple aspirations of
various people influencing the individual be it family,
friends, living, dead or even the ones yet to come.
A building can not turn into a home without having
Several strings and attachments to it. This Project
aims to create an intelligent and smart system which
can understand the users as people and help them
to buy or rent a house taking care of his background,
His aspirations and his experiences.
Setting Up the Project
The aim of the project is seeing the user as
a network of influences be it in a form of his
family, friends, dreams or aspirations.
11

16. The following Ideas were evaluated on various
parameters for a better real estate experience
Artificial Intelligence based discovery pattern
Collaborating and collection oriented search
Universal “Search”
House Hunting Assistant
Intelligent and personalized Discovery
Evaluation of Ideas
From among the real estate sector, the users are as
much varied as our geography itself. So to accommodate
the usability of a portal has to be very simple and
unified.
So, Discovery of real estate has to be easy, quick and
personalized according to the users and their
environment.Understanding the fact that a lot of
users prefer just prefer “typing in” their demands on
the portal the results should be optimized
according to that.
Revenue ModelUsers Related AppsTechnologyMarket 12

17. Project Specific Evaluation
To establish a design research Structure
Housing as a start up does not have a strict research
process where they use conventional ways of design
research and timelines. So this project will also set up
a new way of research for them
A Unified Flow
The flow of the applications are varied and the
mindset of emerging users would have to be aligned
with better solution to embark their search
Upbeat Technology Scope
With the emerging trends and scope of futuristic
advancement in technology, influential ideas like Siri,
“Ok Google” or Cortana on which users now heavily
rely upon, the scope of the project was to integrate
and explore such a world of Ideas in real estate
world to assist the user.
Unique User Pattern Study
The real essence of the real estate sector comprises of
users emerging from different backgrounds, culture,
age, geography & thought. So one of the main aim of
the project is to parse them under a scanner.
Design Novelty
From among the real estate sector, other applications
also rely upon a mechanical way of searching a house.
This design integration will not only change the flow of
the user’s task but also increase the scope of the
product.
13

18. GENE
Introducing
e
To ease the discovery of the Houses and
projects, Search was used as a primary method
of the user journey.
Genie Search or Magic Search would help
the user by suggesting, recommending and
providing answers based in a more
personalized and intelligent manner.
The search will not just include giving search
results but will also help to gather data for the
user base. It Will help us to study the real estate
sector of India and the thinking pattern of the
user across the country.
14

19. Project Scope
Duration : 5 Months ( April ‘15 - Aug ‘15 )
Project Brief
To tackle various user’s aspirations, their mindset and
their different task flow, the object of Genie search is to
collaborate all the various method of discovery and
recommendations for the user and to personalize the
experience of finding a new home.
To bring out data and to streamline the results
according to data fetched at micro and macro level.
Genie integrates social networking features such as
Instagram, Facebook and foursquare as well.
Where now the will not only look at houses as building
as an asset of city, locality or people.
The feature is suppose to evolve into a standalone
product of data based real estate recommendation and
discovery application in all the existing platforms of the
housing family.
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20. 16

22. Design Process
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23. Design Process for Genie
Indentifying the need of
the project brief
Personas
Scenarios
User
Research
Task Flow Wireframes Visual Design
19

24. The Process
Design Process is a sequence of events and activitues that has a start and an end point
Following a design process helped in the following ways
a. It helped in the planning of the project
b. It helped in coordinating with the Product and the Design Team
c. Helped in taking Genie from ideation to implementation
April - May May - June June - July July - August
Information
Architecture
Wireframes
Visuals and
Prototypes
User Personas
Ideation and
Concept
Generation
Task Flow
Background
Research
Present Task
Analysis
Secondary
Research
Primary
Research
Ethnography
User Research
Present Data
Analysis
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26. 22
User Research
User Study
Background Research

27. Background Research
Real Estate in India
Property comprised of land and the buildings
on it, as well as the natural resources of the land
including uncultivated flora and fauna, farmed
crops and livestock, water and minerals. Although
media often refers to the "real estate market" from
the perspective of residential living, real estate can
be grouped into three broad categories based on
its use: residential, commercial and industrial.
The real estate sector in India has come a long way
by becoming one of the fastest growing markets in
the world. It is not only successfully attracting
domestic real estate developers, but international Investors
as well. The growth of the Industry is attributed mainly
to a large population base, rising income level,
and rapid urbanisation.
23

28. Digital Real Estate
Real Estate is Going live with upcoming trends in
this never ending Industry.Digitization has impacted
the Indian Real Estate industry in a big way. When
the consumer is connected 24x7, the industry has
no option but to empower them with information in
easy to use modules with an option to compare and
make informed decisions.
Real estate is an information-intensive business.
Agents connect buyers to sellers through control
and dissemination of information. Agents are valued
for the information skills they bring to making both
listings and sales. Since houses are expensive, not
easily describable and infrequently bought or sold,
most individuals still feel the need for assistance
with this transaction from a professional
24

29. Background Research
Search
səːtʃ/
try to find something by looking or otherwise
seeking carefully and thoroughly
25

30. Background Research
Search Query
Transactional Search Queries
Information Search Queries
Navigational Search Queries
the words and phrases that people type into a
search box in order to pull up a list of results –
come in different flavors. It is commonly
accepted that there are three different types of
search queries:
26

31. Transactional Search Queries
A transactional search query is a query that
indicates an intent to complete a transaction,
such as making a purchase. Transactional
search queries may include exact brand and
product names (like “samsung galaxy s3”) or be
generic (like “iced coffee maker”) or actually
include terms like “buy,” “purchase,” or “order.”
Information Search Queries
Wikipedia defines informational search
queries as “Queries that cover a broad topic
(e.g., colorado or trucks) for which there may
be thousands of relevant results.” When
someone enters an informational search
query into Google or another search engine,
they’re looking for information – hence the
name
Navigational search queries
A navigational query is a search query entered
with the intent of finding a particular website
or webpage. For example, a user might enter
"youtube" into Google's search bar to find the
YouTube site rather than entering the URL into
a browser's navigation bar or using a
bookmark.
Background Research
27

32. Search Patterns
Feature Search
Relational Search
Implicit Search
Natural Language Searches
Subjective Search
Compatibility Searches
Thematic Search
During the usability study, the test subjects
were observed to rely heavily on e-commerce
search queries that included a theme, feature,
relation, or symptom
Background Research
28

33. is the Query Qualifier that is most commonly
submitted as a standalone query
Relational Search
is when a user submits a query that uses
regular spoken language. Ideally, the search
engine is able to interpret the meaning of this
query and return highly relevant results, going
well beyond simple keyword matching.
Natural Language Search
where they input the name or brand of a
product they own along with the type of
accessory or spare part they are looking for,
such as “sony cybershot camera case”
Compatibility Search
are often a little difficult to define because
they are inherently vague in nature – they
often include fuzzy boundaries or
categories of intended usage
Thematic Search
requires the site to make use of all
available environmental data in order
to accurately infer any implied aspects
of the user’s query.
Implicit Search
when the user includes one or more
features in their search query that they
want the product to have
Feature Search
What exactly constitutes a “high-
quality” product? Or a “nice-looking” or
“cheap” one? Answers to such
questions will necessarily be subjective
in nature
Subjective Search
Background Research
29

34. Artificial Intelligence ( AI ) Intelligent Presonal Assistant
An intelligent personal assistant (or simply IPA) is a
software agent that can perform tasks or services
for an individual. These tasks or services are based
on user input, location awareness, and the ability to
access information from a variety of online sources
(such as weather or traffic conditions, news, stock
prices, user schedules, retail prices, etc.).
Examples of such an agent are Apple's Siri, Google's
Google Now, Amazon Echo, Microsoft's Cortana,
Braina (application developed by Brainasoft
forMicrosoft Windows), Samsung's S Voice, LG's
Voice Mate, BlackBerry's Assistant, SILVIA, HTC's
Hidi, IBM's Watson (computer), and Facebook's M.
The central scientific goal of AI is to understand the
principles that make intelligent behavior possible in
natural or artificial systems. This is done by
the analysis of natural and artificial agents;
formulating and testing hypotheses about what it
takes to construct intelligent agents; and designing,
building, and experimenting with computational
systems that perform tasks commonly viewed as
requiring intelligence
An agent is something that acts in an environment -
it does something. Agents include worms, dogs,
thermostats, airplanes, robots, humans, companies,
and countries.
Background Research
30

35. Natural Language Processing (NLP)
Natural Language Processing (NLP) refers to AI
method of communicating with an intelligent
systems using a natural language such as English.
Processing of Natural Language is required when
you want an intelligent system like robot to
perform as per your instructions, when you want
to hear decision from a dialogue based clinical
expert system, etc.
The field of NLP involves making computers to
perform useful tasks with the natural languages
humans use. The input and output of an NLP
system can be −
Speech & Written Text
Natural Language Generation (NLG)
It is the process of producing meaningful phrases
and sentences in the form of natural language
from some internal representation.
Natural Language Understanding (NLU)
Understanding involves the following tasks
Mapping the given input in natural language into
useful representations.
Analyzing different aspects of the language.
Components of NLP
Background Research
31

36. Semantic Web
The Semantic Web is an extension of the Web through
standards by the World Wide Web Consortium (W3C).
The standards promote common data formats and
exchange protocols on the Web, most fundamentally
the Resource Description Framework (RDF).
According to the W3C, "The Semantic Web provides a
common framework that allows data to be shared and
reused across application, enterprise, and community
boundaries".The term was coined by Tim Berners-Lee
for a web of data that can be processed by machines.
While its critics have questioned its feasibility,
proponents argue that applications in industry, biology
and human sciences research have already proven the
validity of the original concept.
Background Research
author
birthplace
located intype
temperature
type
date of birth
type
CD Albums
All Music
Geo Almanac
Weather Channel
type author
Yo - Yo MaApplachian
Journey
Tavener
Music
Album
Musician
Paris, France
10 / 07 / 55
FranceCity
62 F
Fig 3.1 : A segment of the Semantic Web pertaining to Yo-Yo Ma
32

37. Predictive Search
Predictive search is based on the semantic prediction
of needs; predictive search engines return results
based on the current context, the historical behavior,
aggregated user behavioral patterns and the active
solicitation of information.
By combining these indicators, search engines can
predict the user’s current intent and serve the best
possible answer. We don’t have to shout at our
devices at all when predictive search serves us results
we have not even had time to query.
Background Research
33

38. Background Research
34
Fig 3.2 : structured data and semantic search leading to prediction

39. Case Studies
Background Research

40. Google
Google’s Knowledge Graph
Google Now and IOT
Facebook’s Graph Search
Facebook
Case Studies
36

41. Google’s Knowledge Graph
the “knowledge graph” is a databank that collects millions
of pieces of data about keywords people frequently search
for, and the intent behind those keywords, based on the
already available content. With the knowledge graph, users
can get information about people, facts, and places that are
interconnected in one way or the other.
To make your learning easier, just go to Google and search
for “what is the knowledge graph?” The answer is displayed
right there – and that’s also what the knowledge graph does.
Search Result Fetched by
Knowlegde Graph
Case Study : Google
37

42. Another example: Go to Google and search for “famous
actors” right now. The picture carousel that appears at the
top is a good example of a knowledge graph result.
Apart from widening your own personal knowledge base,
you can also take advantage of the knowledge graph to get
more search traffic to your site
Search Result Fetched by
Knowlegde Graph
Google’s Knowledge Graph
Case Study : Google
38

43. Steps InvolvedThe Knowledge Graph enables you to search for things,
people or places that Google knows about—landmarks,
celebrities, cities, sports teams, buildings, geographical
features, movies, celestial objects, works of art and more—
and instantly get information that’s relevant to your query.
This is a critical first step towards building the next
generation of search, which taps into the collective
intelligence of the web and understands the world a bit
more like people do.
Google’s Knowledge Graph isn’t just rooted in public sources
such as Freebase, Wikipedia and the CIA World Factbook. It’s
also augmented at a much larger scale—because we’re
focused on comprehensive breadth and depth. It currently
contains more than 500 million objects, as well as more than
3.5 billion facts about and relationships between these
different objects. And it’s tuned based on what people
search for, and what we find out on the web
“
“
1. Find the right thing
3. Go deeper and broader
2. Get the best summary
Google’s Knowledge Graph
Case Study : Google
39

44. 1. Find the right thing
Language can be ambiguous—do you mean
Taj Mahal the monument, or Taj Mahal the
musician? Now Google understands the
difference, and can narrow your search results
just to the one you mean—just click on one of
the links to see that particular slice of results:
Case Study : Google
40

45. 2. Get the best summary
With the Knowledge Graph, Google can better
understand your query, so we can summarize
relevant content around that topic, including
key facts you’re likely to need for that
particular thing. For example, if you’re looking
for Marie Curie, you’ll see when she was born
and died, but you’ll also get details on her
education and scientific discoveries
Case Study : Google
41

46. 3. Go deeper and broader
Finally, the part that’s the most fun of all—the
Knowledge Graph can help you make some
unexpected discoveries. You might learn a new
fact or new connection that prompts a whole
new line of inquiry. Do you know where Matt
Groening, the creator of the Simpsons (one of
my all-time favorite shows), got the idea for
Homer, Marge and Lisa’s names? It’s a bit of a
surprise:
Case Study : Google
42

47. Google Now and IOT
43
Google Now embodies the true possibilities of
predictive search, serving as a personalized
computer assistant that can predict your needs,
wants, and deep desires.
For some, Google Now is some strange sorcery, as it
delivers important information about the traffic on
your morning commute, your updated flight
itinerary, and the results of last night’s hockey game
on your phone, without you even asking. How did it
even know that!?
It’s not magic or mind prediction – the Wizard of
Googs is hidden in the Emerald City, behind the
curtain, pulling all the strings, smoke, bells, and
whistles. In order to provide this relevant info that
relates to you and only you, Google uses your
private data, accessing (with your permission of
course) your Gmail and other info in order to keep
tabs on things like flight reservations and hotel
bookings.
“
“

48. Google Now and IOT
Google Now leverages the data it collects and
aggregates from its users through Search, Mail, Maps,
Calendar and Google Plus — basically everything that
is using a Google login.
It understands who you are, what you are doing and
where you are doing it to predict what you want
based on user behavioral patterns. (Microsoft’s
Cortana does not have that breadth of data points
and aggregation and bases its results on user-set
preferences.)
Geolocation is also a huge context factor for Google
Now results. With smartphones and wearables, users
are no longer geolocated by an IP address but by the
device’s physical location and its motion. Google Now
uses location history to learn where you live or work;
tracks your movements based on GPS check-ins; and
uses date, time and your search history to serve you
highly relevant traffic and weather reports, local
restaurants, travel recommendations, flight schedules
and more.
“
“
44

49. The Facebook graph is the collection of entities and
their relationships on Facebook. The entities are the
nodes and the relationships are the edges. One way
to think of this is if the graph were represented by
language, the nodes would be the nouns and the
edges would be the verbs. Every user, page, place,
photo, post, etc. are nodes in this graph. Edges
between nodes represent friendships, check-ins,
tags, relationships, ownership, attributes, etc.
Facebook’s Graph Search
Case Study : Facebook
45

50. Designing a System for Graph Search
PPS and Typeahead search Facebook entities based on their
metadata--primarily using their name (title). The kinds of
entities searched are users, pages, places, groups,
applications, and events. The goal of Graph Search was to
extend this capability to also search based on the
relationship between entities--meaning we are also
searching over the edges between the corresponding nodes.
We chose to use natural language as the input for the
queries, as natural language is able to precisely express the
graph relationships being searched over. For example:
Restaurants liked by Facebook employees
People who went to Gunn High School and went to Stanford
University
Restaurants in San Francisco liked by people who graduated
from the Culinary Institute of America
For example:
Restaurants liked by Facebook employees
People who went to Gunn High School and went to Stanford
University, Restaurants in San Francisco liked by people who
graduated from the Culinary Institute of America
Case Study : Facebook
46

51. Facebook’s graph search designer, Maschemeyer is the first
to admit that the product is a little different. "It’s really about
coming to results, and going deep on that set of results, and
discovering things and making connections that you might
not have even known you could make or finding things you
didn’t even know you could find," he says. Web search,
meanwhile, surfaces results for concrete queries and
assumes that you’ll leave the site as quickly as possible. The
same design paradigm isn’t suitable for them both.
Maschmeyer helped take Graph Search out of the box.
The search box, that is. Instead of the familiar white query
box that previously controlled Facebook search, people type
their requests for Graph Search into the site’s blue
masthead. Their queries become the titles of search results
pages. "Really any view on Facebook is a kind of search,"
Maschmeyer says of the decision. "Newsfeed is posts by my
friends. My timeline is like my name, all about me. And then
graph search is every other view you could imagine, every
other cut of that graph that you could potentially dream up.
Case Study : Facebook
47

52. Giving search results titles makes them feel more like
alternative views of Facebook that are, like Timeline, meant
for browsing and exploring rather than search results meant
for instant departure. In this way, it provides an opportunity
to show users they can search for something for which they
may have not been looking. When users start typing into the
masthead, for instance, a drop-down menu suggests
searches for them to demonstrate some possibilities. If they
type "friends of my friends," for instance, it might suggest
searching for those who "live in my hometown" or "went to
my college" or "work at my company."
Case Study : Facebook
48

53. Benchmarking
Background Research

54. Trulia
Compass
Lovely
Foursquare
AirBnb
Lonely Planet
Benchmarking
52
53
54
55
56
57
51

55. Trulia
An International App, Trulia finds
real estate based on your location
and neighborhood.
With advanced Data based about the
City, locality and demography, Trulia give the
user enormous information related to there
lifestyle to make a better decision.
Trulia also offers a feature to search
and collaborate with your friends and
family with hunting for a house making
it a Pinterest for real estate.
Benchmarking
52

56. Compass
Compass offers a similar featured
search for rentals with map based
search along the neighborhood.
Compass also offers curated picks
along the locality and the City.
The Visual Aspect of the Site is very
clean and makes a good impact on the user
Benchmarking
53

57. Lovely
Another Rental platform with a
very clean and neat design with easy to
use filters and wide range of filters so
users can filter the results along their need.
Offers filter by Image search of the house and plans
as well.
Benchmarking
54

58. Foursquare
Foursquare offers on locations
reviews to users from the users themselves.
The data is crowdsourced and channelized
accordingly. This is an excellent example of a City
Guide with respect to it’s hangout and exploratory
places.
Benchmarking
55

59. Airbnb
Airbnb is now a renowned name in the travel
discovery app. With it’s “one less stranger” slogan
airbnb offers visual relaxation to the users when they
cross national and international borders.
This gives an opportunity for the renter and the
rentee to shake hands and earn profit.
Benchmarking
56

60. Lonely Planet : App
Lonely planet has curated reviews about cities and
nearby landmarks. It has a easy to use and a simple
interface with a “feel good” look and feel. Users have
an option to download the City Guide as well.
Benchmarking
57

61. Competitor’s
Analysis
Background Research

62. CommonFloor
99Acres
MagicBricks
60
61
62
Competitor’s Analysis
59

63. CommonFloor
CommonFloor being one of Housing’s Direct
Competitors went on selling and renting
houses on a similar stream. There main focus
being more on Houses being “Bought” than
“rented”. They had a stand up with there new
“live-in” Tour Guide for the users.
The Visual color scheme and Layout being on
similar lines with housing and it’s Competitors.
Though abundant in data base they lack trust
in no. of houses and projects covered.
Competitor’s Analysis
60

64. 99Acres
One of the standalone veterans in real estate.
99acres leads in the data base of houses and
projects. The design language being on a
similar note, they showcase their results more
on SEMs rather than showcasing filtered
results. There new feature consisting of
recommended listings and collecting for the
users is definitely helpful, specially after their
recent UI Revamp.
Competitor’s Analysis
61

65. MagicBricks
With the huge Times Data and Surveys on their
side they have a good opportunity to tune
down results for the users and use featured
paid listings as well. MagicBricks has good
quality and ample amount of reports and user
base which are available for reference purpose
as well.
Competitor’s Analysis
62

66. Competitor’s Analysis
63

67. Secondary
Research
User Study

68. The Home Scout
Refining Task
Identifying User
66
67
68
User Journey
Property Profile
79
84
Secondary Research
65

69. The Home Scout
The house search query is an integral part of the house
finding system. If we look at the mindset of people who start
looking for a house, we would come to a conclusion that
people often don't believe in the idea of Online House
Search.
One does not simply get satisfied until and unless he himself
would go there and interact with the physical space around
the house to understand and quench his curiosity about the
his new home and imagine himself settling in that space.
To understand his demands and the types of questions
which arise in his mind we would have to understand the
network of the user first.
Secondary Research
66

70. Refining the Task
Genie Search is suppose to close the gaps between the
users mind map and the system map leading them to
their desired location. To understand what Genie Search
should cater to one has to look upon what all paradigms
does the users experiences while conversing with the
machine. When it come to the tradition way of house
search one has to go through a lot of mental work before
he begins his study of finding a house. Genie Search
should cater to the same result that he gets after going
through all the mental and the physical work on his own.
So the key here is to tap his expectations with respect to
understanding of what he feels is satisfactory for him.
The Idea here is to understand his needs when he needs
to tell someone what he requires. To empathize with his
requirements, understand his limitation and gaps in
knowledge. The Search comprising of typing in his
requirements to displaying information that the machine
has sensed for him.
Secondary Research
67

71. Identitifing the Users
I began with Creating a User Scenario in my mind and
figuring out a user journey for him from my experience by
discussing with people around the workplace.
Which lead me towards a Customer Journey map of a
user and how Housing tries to deliver it through it’s digital
platform.
Secondary Research
68

72. Identifying the Basic Need :
The Purpose of anyone who lands upon using housing is
seeking a house. That's the basic drive which brings
people to this service.
The Basic services which the applications provides you is
Rent | Buy | PG and Hostel | New Projects | Home
Loans | Serviced Apts.
Secondary Research
69

73. Identitifing the Users
People looking out for houses lies within a huge
bandwidth. There is no specific Age Group which this
service can specifically point at and target. However there
can be numerable Use Cases with which the service
system could be mapped with.
Secondary Research
70

74. User Classification
Based on the age group
Secondary Research
The Bachelors ( 15 - 27 yrs )
The Middle Ages ( 28 - 50s years )
Old Age Users ( 60+ years )
71

75. The Bachelors ( 15 - 27 yrs )
Consists of a house search where they plan out things for
the present / a predictable scenario. There is a specific
reason for them to shift with specific demands and lesser
flexibility towards alternative solutions.
With respect to Gender, the specifications and demand
diversifies and leads to a whole lot of different decisive
factors for the user to come up to a specific conclusion.
For this Age Group the needs sometimes is quite minute
which forms more important factors as a reason to shift.
Secondary Research
72

76. The Middle Ages ( 28 - 50s years )
They consists of the paradigm of “A Mutual Decisive
Factor”. Here is a group where responsibilities fuse
together along with expectations, demands and
requirements multiplied by few more numerals as the
decisions taken at this stage is a mix decision of other
people attached to that person as well.
The diversification between the two genders diversifies in
their opinion. As the requirements and demands matures
with experience and age.
Secondary Research
73

77. Old Age Users ( 60+ years )
A critical user group where the foundation knowledge of
digital world also plays an important role. This user group
is experienced enough & have seen different mediums
and trends go by and have lived by it as well. They act as a
seeker as well as they sought decisions for others as well.
They are used to a specific medium of search and have
built their trust upon it and ready to share that immense
amount of experience too.
Secondary Research
74

78. Composition of Users
Individual Requirement
Where the decision making revolves around the
conditions of one person who himself goes out to initiate
his process of house search and contacts various
mediums and modes in his path.
Fix on Budget | Location Specific | open for rents / PGs
too
Secondary Research
75

79. Family of Two
Here the minds of two people work in sync to resolve
their present and try to look forward to their future
scenarios / conditions as well. Things like expansion of
Family also comes into play hence an iteration towards
space and locality becomes important.
Composition of Users
Fix on Budget | Location specific | Looking out for Buy /
Rent | Space Specific
Secondary Research
76

80. Joint Families
Here the factor of an Indian Family comes into play which
takes house search to a whole lot different level. ‘We’
Factor combine with billion more reasons and opinion
fused together.
Composition of Users
Flexible Budget | Connectivity preferred | Buy prefered
over Rent |Bigger Space Required
Secondary Research
77

81. Definitive User : Where the user knows exactly where,
why and what he wants before he initiates his house
search. Tips and Tools might help but he has already been
through all the tangible and intangible requirements.
Exploratory User : Where the user is open to options and
is looking out for even more opinions though his
requirement. He still hasn’t landed on his perfect
destination.
User by Preference :
Secondary Research
User by demography and Ethnicity
Due to the diversity in our country every city has a
different composition, cultural aspect and mind set which
have to be tapped in order to customize the house search
journey for the users. Every City has a different
composition by similar pattern of decision making
78

82. Secondary Research
Pre-Hunt
While searching for a house the user undergoes the
following flow
This creates a specific profile of a user .
User Journey
79
Understand his Need / Requirement behind his decision
to move to a new place
Evolving Expectations through his experiences.
Understanding his Limitations.
Integrating his thoughts to create a wish list for his new
house.
Sensing the urgency / time required to get the house.

83. User Journey - Pre hunt
Mapping out Points and Pointers as suggested by others.
Streamlining his list of opportunities
Stepping to make a better narrowed down decision
Secondary Research
80

84. Secondary Research
On-Hunt
After understanding and tuning his needs and
expectations.
The user performs this task through various checkpoints,
nodes and mediums.
Although these mediums keep interchanging through out
the journey .
Some of the nodes he goes though are given below -
1. Self Search
2. Agents / Brokers
3. Friends / Friends of Friends /Family
User Journey
81

85. Secondary Research
User Journey
Self Scout : To Begin with, on this stages the users explores
various modes to attain information on houses which fall under
his wish list of preferences of the type of accommodation.
mediums like :
Newspaper Listing
Advertisements on the notice boards
Online Listing
Word of Mouth
Through an Agent : For people who are in lesser contact with
people around a specific locality / City usually tilt towards this
option. Here the agent takes full charge of showing his
customers around his shortlisted places that he has in hand.
People usually try to to avoid getting a house through them as it
often never ends on a satisfactory note as it involves exploitation
of trust, words and money. Trustful agents though are still
banked by many as people don’t fear any loss due to them.
82

86. Secondary Research
Through a Friend : This is the most trusted option by people
due to the familiarity of the person’s lifestyle, mindset and
opinion. If someone has known you for a while and then they
optimize a list for you then you tend to lean towards their
decision more than going for a broker or doing extensive
homework.
Through the User Profile created while in Pre hunt stage by the
the mode could become more sensible of the user and hence
can present a better medium for him.
The things which interact with each other as consumer and
products are
Users and the Property
For that we would have to understand the profile of the property
as well and how well we could match them together.
83

87. Secondary Research
What
About the tangible properties of the house. This Category
describes what the house is about and talks about the
composition if the house. Some of the categories which comes
under this category are
-Type of the house (given in the new property feature)
-Age of the House
-Built up Area
-No. of rooms / Kitchen
-Facilities in the house
-Amenities
-Furnishing
Property Profile
To Define a Property one would have to wonder about it’s
discrete features of being
-What
-Where
-When
-How
-Why
84

88. Secondary Research
Where
This category would depict the location properties of the house.
Its about where the house would be situated and what all
facilities would be available in that. Features mapped under this
categories could be-
• Social amenities
• Locality
• Commutation
• neighborhood
• Accessibility
When
When describes that property through a dimension of
time. Speaks about the characteristics of the house
through different phases so that it could be assessed
accordingly.
e.g A house might have different appearances with the
paradigm of time change. So just to make sure people
like to have a look at the house during various intervals
of time of the day.
85

89. Secondary Research
Why
When the user profile syncs and tunes with the property profile
the user seeks a better way to understand that why this
property would be more suitable for him as compared to the
other houses.
The task is that the service helps to reduce the number of steps
one takes to reach towards his perfect house and understands
and agrees with the ‘why’ of the house in accordance/ due to the
former points.
How
How depicts the process of how to acquire the house and
restrictions and limitations tagged along with the house.
86

90. Property Profile
To Generalize the categories of the user, the categories could be
divided into various paradigm describing the following concepts
of a house
What's Inside the House ?
-Basic Services - Facilities - Furnishing- Amenities -
Features - Aesthetics
The House?
-Type of House - House Configurations - Area/Space - Features -Rooms/
Kitchen/Bathroom
How’s the neighbourhood ?
-Walkthroughs around the building - Social Amenities -
People - Utility Stores
-Parking - Utility Features - Luxury Features
What about the locality ?
Connectivity - Society - Stations - Modes of Commutation - Reaching out
- Specific Destinations - Routine Travels
Secondary Research
87

91. User Task
User Study

92. Analyzing the task
Rent a house
Location
90
92
Composition
Budget
93
93
User Task
89

93. Analysing the task
From the homepage when the user
understands what service he would like to
go for he goes and understand the
parameters he has to enter.
Here we bifurcate into two types of users -
The Real Expert: He knows where to go,
What type of houses are best suited for
him he can make more of the house
information which is provided to him. But
since he is experienced enough he would
scrutinize his results at different level than
others.
The Naive User : He’s new to this fair. More
information in a form of more texts,
iconographies is required for him to even
make sense of the media in which the
house is shown to him.
User Task
90

94. Analysing the task :
To rent a house
The task flows for these two very broad
categories becomes very different when he
is looking online for a house and ending
being satisfied enough to take one home.
These categories are further categorized
into smaller categories which leads to
another level of demand and speculations.
Presently, the house search (on the
website) begins by entering different
parameters like
Location ( City, Locality, Present location)
House Composition ( in terms of BHK )
Budget ( in Rupees )
User Task
91

95. Analysing the task :
Location
The Location search depends upon Locality
and Landmark.
The search query could either be a Location/
Place or a landmark.
e.g Powai, Mumbai, Maharashtra is auto
suggested when one tries to type in Powai.
e.g Galleria is depicted with respect to
landmark.
User Task
92

96. Analysing the task :
Composition ( BHK )
When one selects the house
composition a dropdown of checklist
comes up showing the various
option that one has. The user could
select multiple types of houses as he
wants to seek.
User Task
93
Analysing the task :
Budget
There is a set value that the user
would have to enter. The max
value depends further upon the
min value that the user enters.

97. Discovery
User Study

98. Discovery Search 96
Discovery
Analysing Discovery 97
95

99. Discovery Search
Discovery is a method of search where the user
doesn't have to go through mechanical search by
adding parameters and sorting the results but the
user discovers his sought after result just by
browsing through the homepage itself.
Discovery is a great way to introduce the user to
various houses in a particular locality, or a even
various localities in a City with specific amenities.
User Task
96

100. Analysing the task :
Discovery Search
A good example of relevant discovery search
along with personalization and
customization of user’s profile is Amazon.
Amazon puts every recommendation that
the user might wanna buy and does end up
buying.
On Contrary, the present housing homepage
provides irrelevant details on their company
and work culture.
Introducing Collections is a swift way to walk
user through his needs through predictive
recommendation system.
User Task
97

101. Search
Research
User Study

102. Analyzing Task
Map View
List View
100
101
102
Filtering
Shortlisting
103
105
Search Results
99

103. Analyzing the task :
Search Result
After entering the parameters,the user find the
search result in the two different forms
Map View
( Where the users find his options
displayed on a map)
List View
( Where the user finds a list of options in a form
of a List )
User Task
100

104. Analysing the task :
Map View Results
Viewing map view for the listings
gives you a visual aid as the location
markers not only show the respective and
relative positioning of the house,
but also defines the under laying locality
and relative proximity to other commute
and lifestyle nodes.
The challenge here is to depict the boundaries inter
locality clearly such
the visualization will make sense to the
user of different personas.
eg. for a bachelor with different demand as
compared to a family guy.
User Task
101

105. Analysing the task :
List View Results
The results are displayed in a list format
where the information about the houses
are stacked vertically on top
of each other which are cognitively easily
to read in a single go as compared to a
map view.
Each module is accompanied by various
particulars about the residence.
The challenge here is the maintain it’s
hygiene and keep the information delivery
clean.
User Task
102

106. Analysing the task :
Applying Filters
One of the most tedious and essential tasks for
a user is applying filters.
Filters are basically a mechanical term used to
understand the users exact demand and
requirements. Optimizing filters is a very
essential step towards customizing a digital
portal for a human.
Search also add filter tags, so can there be a
ways to integrate both of them?
User Task
103

107. Analysing the task :
Sorting Results
First things First.
To sort the results for the user is beneficial not only
for the user’s sake but also for the sake the product’s
market strategy.
Featured Results placement, Budget sorting,
Popularity sorting, Relevance Sorting, Freshness
sorting are the standard sorting view which asks
users to pick an order to display their result
User Task
104

108. Analysing the task :
Shortlisting
At a first glance, our users might not gonna fix upon a
single particular project / House and go forward to
buy it there itself. The user might wanna compare it
with other projects or just wanna keep it in handy for
afterwords.
To Save is to shortlist.
Shortlisting is not only useful of the customer but
also for the product to become more intelligent about
the user. So the next time the user log back in he
finds the houses which are more suited for his
profile.
User Task
105

109. Analysing the task :
Viewing Elements
To the ideal result display we need to understand to
things which drives our decision, Things what we get
and the things that we are looking for. Which brings
us to important criterion of result display:
> Viewing result
> Viewing your search tags
User Task
106

110. User Task
105

111. Primary
Research
User Study

112. Forms and Surveys
Data Analysis
User Studies
112
114
115
Analysis 116
Primary Research
109

113. Primary Research
110

114. Primary Research
Setting up Primary Research, required a process to be followed
a. It helped in proper planning of research
b. It helped in coordination with the users recruited
c. Helped in finding meaningful insights and further building upon it.
User
Interview
Results and
Analysis
Prepertaion
Forms and
Surveys
Analysis
Recruiting
Users
Indentifying the need and scope
of the Research
111

115. Reaserch Process
Forms and Surveys
The Project, Genie, Is based on user’s mindset and
understanding the needs of the users. The whole crux
of the project lies in understanding the user and his
needs. Real Estate is a vast topics which deals with
geography, so the research particularly focusses upon
ethnographic understanding of the user’s and their
needs.
To begin with a formal research, a plan has to be set
sail. The plan would have to develop as a result of the
researcher’s understanding about the product.
112

116. Creating Forms and survey are a part of developing
the research which involves various methods like
conducting gorilla Cafe Studies and sending across
surveys through the mode of internet.
While creating a Survey, an understanding of the
product has to be there which was developed during
the secondary research of the product.
A very important step towards creating a process of
the unstructured review is to understand the usage
pattern of the users in the existing platform. Data
analytics becomes a very essential tool towards this
step.

117. Forms & Surveys
Data Analysis of the present
platform.
Tool used to capture all the Data Analysis is Google
Analysis which reported all the touch points and
inferences of the user’s mindset.
The tool uses real time data and sends out stats upon
which I further developed my research.
Besides, Google Analytics, The Data Science Lab (DSL)
of Housing came into play as well.
DSL recorded the activities of the users in various
cities and provided various insights, which further
helped to streamline my research process.
114

118. Forms & Surveys
User Study.
Things which makes the user decide upon a specific
house
Behaviour w.r.t the Property
Knowledge
His Mind Map
Experience
Ethnography
115

119. Forms & Surveys
Data Analysis of the present
platform.
After conducting Secondary research.
Data and Stats were looked upon which focussed
upon the user’s actions and behavior revolving
around the existing flow.
116

120. Forms & Surveys
Data Analysis of the present
platform.
Ethnographic Mobile app analytics are as follows
117

121. Forms & Surveys
Data Analysis of the present
platform.
118

122. Forms & Surveys
Data Analysis of the present
platform.
119

123. Forms & Surveys
Data Analysis of the present
platform.
Form the data we can clearly figure out the cities
which are looking out for RealEstate at housing.com
Some of the to cities include:
Delhi, Bangalore, Mumbai, Pune, Kolkata and
hyderabad
So understanding the task flow from users belonging
to these states were considered and taken forward.
The data of these cities were drilled down and further
analytics were looked upon. Therefore, paradigms
such as composition, Price and demand of the locality
were looked upon
120

124. Forms & Surveys
Use Case of Mumbai is considered
as a task flow
121

125. Data Analysis
Demand
Online demand & supply of each locality
was studied and high demand
localities like Ghodbandar Road,
where demanded inventory didn’t
meet the available entries, were
studied over “Apartment Type” & “Budget”

126. Data Analysis
Conclusion
The following data concludes a pattern of demand with respect
to each city within the parameters of Budget and Composition
of the flat.
The idea here becomes to understand a stronger pattern
involving more parameters like relating to the user profile.
• Neighborhood Demand
• Connectivity Demand
• Services Preference
• Facilities Available
121

127. User Research
Conclusion
The Idea of Research for that would be to understand their
mindset and figure out a trend in their preference with respect
to their search.
So to begin with the interviews I created a following
questionnaire -
122

128. User Research
Questionarre
Creation
123

129. User Research
Questionarre
Creation
124

130. User Research
Analysis
The following was the result of the survey with the help of 54
user set from different cities across India.These users have
gone through the process of house hunting and are varied
across different age groups. This helped me creating and
focussing upon a particular user group and touch points in the
further process of user research
125

131. User Research
Forms and Survey
Understanding the Background of the User
Type of house user has so to understand the process
he through while obtaining it.
126

132. User Research
Forms and Survey
Understanding the Background of the User
127

133. User Research
Forms and Survey
Understanding the Journey of User
To seek what was the trigger point of the user’s final
descision.
128

134. User Research
Forms and Survey
Understanding the Journey of User
To seek what was the trigger point of the user’s final
descision.
129

135. User Research
Forms and Survey
Understanding the Journey of User
To seek what was the trigger point of the user’s final
descision.
130