Smart Home - Papua New Guinea

Papua New Guinea

The Smart Home market in Papua New Guinea is anticipated to witness substantial growth in the coming years.
By 2024, the projected revenue is estimated to reach US$9.3m.
This growth is expected to continue with an annual growth rate of 12.51% (CAGR 2024-2028), resulting in a projected market volume of US$15.0m by 2028.
Moreover, the number of active households in the Smart Home market is predicted to reach [users_currentlayer_yearend] by 2028.
This indicates a steady increase in household penetration, which is forecasted to be 3.1% in 2024 and is expected to reach 5.1% by 2028.
Currently, the average revenue per installed Smart Home market stands at US$153.20.
This figure demonstrates the potential for revenue generation from each individual Smart Home market installation.
In a global perspective, United States leads the market with the highest revenue generation.
In 2024, United States is projected to generate a revenue of US$38,800.0m, surpassing other countries in the Smart Home market.
Papua New Guinea's smart home market is experiencing slow growth due to limited infrastructure and high costs of implementing smart technology.

Key regions: United States, United Kingdom, Germany, Europe, Norway

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Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on the sales of smart home products, excluding taxes.

Modeling approach:

Market sizes are determined through a bottom-up approach, building on a specific rationale for each market segment. As a basis for evaluating markets, we use the Statista Global Consumer Survey, market data from independent databases and third-party sources, and Statista interviews with market experts. In addition, we use relevant key market indicators and data from country-specific associations, such as household internet penetration and consumer spending for households. This data helps us estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited for forecasting digital products and services due to the non-linear growth of technology adoption. The main drivers are GDP/capita, level of digitization, and consumer attitudes toward smart home integration.

Additional notes:

The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russia-Ukraine war are considered at a country-specific level. The market is updated twice a year in case market dynamics change.